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 .
DETAILED ACTION
Response to Amendment
Applicant’s amendment filed November 18, 2025. Claims 1-20 have been presented for examination. Applicant’s amendment has been fully considered and entered.
Double Patenting
2. Claims 1-20 of this application is patentably indistinct from claims 1-20 of Application No. 18/487816 and claims 1-20 of Application No. 18/487812 and claims 1-20 of Application No. 17/834768. Pursuant to 37 CFR 1.78(e) or pre-AIA 37 CFR 1.78(b), when two or more applications filed by the same applicant contain patentably indistinct claims, elimination of such claims from all but one application may be required in the absence of good and sufficient reason for their retention during pendency in more than one application. Applicant is required to either cancel the patentably indistinct claims from all but one application or maintain a clear line of demarcation between the applications. See MPEP § 822.
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the claims at issue are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claim Rejections - 35 USC § 103
3. 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 of this title, 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.
4. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
5. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
6. Claims 1, 4-15 and 19-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over METZLER (US 20250209897 A1) (hereinafter METZLER) in view of Beale (US 20230360517 A1) (hereinafter Beale).
Regarding claim 1, METZLER discloses a building system comprising one or more storage devices storing instructions thereon that (FIG. 1, building surveillance system 1, para 30, facility security surveillance method for security relevant evaluation of an anomalous state of a facility providing dynamic model of the facility and building information model (BIM), surveilling a plurality and continuously generating surveillance data, analysing of surveillance data and detecting of at least one state),
when executed by one or more processors, cause the one or more processors to: ingest device information associated with one or more devices within a building (para 83, generate sensor data, and computing unit comprising a processors and a data storage, para 105, computing unit comprising processors and a data storage, remote computing unit adapted to receive sensor data and to evaluate sensor data in real time);
determine a false alarm likelihood within one or more areas within the building based on the device information (para 15, determine a probability for a false positive of a classification, trigger acquisition of data, surveillance data, para 306, surveillance system determines if surveilled state pattern as potentially security relevant state is “anomalous” (not a normal state pattern) and determines if such an “anomaly” has to be seen “critical” or not, para 122, monitoring and alarming system, wherein false alarms reduced and alerts are pointed to an operator's attention, para 134, system dynamically learns new critical or non-critical events, and thus false alarms are reduced and only increasingly relevant alerts are pointed);
cause a display device of a user device to display the graphical model within a user interface (Fig. 30, 31a-31c, para 13, generative or discriminative probabilistic graphical model comprising Bayesian network, Markov random field, fuzzy logic system, neural network, deep neural network, para 93, graphical processing unit and adapted to run machine learning algorithm on graphical processing unit and data storage comprises a database for storing data related to detected states, para 369, acoustic or optic signal displayed on a screen, animating the human being to react).
METZLER specifically fails to disclose cause a graphical model of the building to include an indication of the false alarm likelihood within the one or more areas of the building.
In analogous art, Beale discloses cause a graphical model of the building to include an indication of the false alarm likelihood within the one or more areas of the building (para 02, methods for building and using a false alarm predicting model to determine whether to alert a user and/or relevant authorities about an alarm signal from a security system, para 15, false alarm predicting model can be managed by the learning module, para 24, learning module can build the false alarm predicting model by recognizing patterns in the historical data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of surveillance sensors adapted for surveillance of facility elements and for generation of surveillance data disclosed by METZLER to use systems and methods for building and using a false alarm predicting model to determine whether to alert a user or relevant authorities about an alarm signal from a security system as taught by Beale to transmit notification signal indicative of alarm signal to devices and response to executing customized response protocol include receiving user input indicating that alarm signal is false alarm or valid alarm or failing to receive user input [Beale, para 0016].
Regarding claim 4, METZLER discloses the building system of claim 1, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: generate one or more device placement recommendations based on the device information and the false alarm likelihood within the one or more areas of the building, the one or more device placement recommendations being configured to reduce the false alarm likelihood (para 06, comparing surveillance data generated at two different times or in the course of time. State derivation means are preferably integrated in the surveillance sensors and/or in the central computing unit. Said otherwise, state derivation means are such means that enable extracting the state of a facility element from the surveillance data relating to the facility element, para 125, surveillance sensor configured to, particularly continuously, surveillance of the facility and to generate surveillance data comprising information about the facility); and
cause the graphical model of the building to include the one or more device placement recommendations (para 192, training data with contextual information, which training data is at least partially synthetically generated and derived form a virtual model, para 22, graphical output within a graphical, three-dimensional, visualization of the facility model).
Regarding claim 5, METZLER discloses the building system of claim 4, wherein the one or more device placement recommendations comprise one or more device overlays overlaid onto the graphical model and depicting a location of one or more recommended device placements within the building (para 192, multiple of the modalities from different sensors can be combined into a single dataset, by which dataset machine learned system is trained of surveillance system arranged at different locations, with different points of view, with different fields of view, para 19, analysis of surveillance data of surveillance sensors, at different locations and times, or state pattern is derived using person tracking, Hidden Markov Model (HMM), para 447, based on such training data, best combination of classifiers or detectors is learned. and learning is done for each setting, (i.e. for each location like indoor vs. outdoor).
Regarding claim 6, METZLER discloses the building system of claim 4, wherein the one or more device placement recommendations include an indication of a predicted security improvement expected from performing the one or more device placement recommendations (para 481, system evaluate bag 60 at the present location is not qualifying for a risky security alert, but only produce a low level security warning or a simple log-entry, such evaluation could be programmed or trained to be classified differently, e.g. at public places, para 493, anomalies by an automatic system, a large amount of data for each of the different sub-tasks (person, door or misplaced item detection) is necessary in as many variations and combinations as possible to have reasonable diversity in the training data).
Regarding claim 7, METZLER discloses a building system comprising one or more storage devices storing instructions thereon that, when executed by one or more processors that (FIG. 1, building surveillance system 1, para 30, facility security surveillance method for security relevant evaluation of an anomalous state of a facility providing dynamic model of the facility and building information model (BIM), surveilling a plurality and continuously generating surveillance data, analysing of surveillance data and detecting of at least one state),
cause the one or more processors to: ingest device information associated with one or more devices of a fire suppression system, the device information comprising device location information and device inspection or maintenance information (para 16, state is derived using at least one of detection and/or recognition of persons, detection of open doors and/or windows, detection of fire and/or smoke, detection of abandoned objects, or recognition of activities, para 481, abnormal and a potential security event to be detected and categorized as such by the surveillance system, once detected and classified as resulting in a critical state, needs further inspection and corrective action to be initiated, para 128, security monitoring system is configured to detect a sequence and/or pattern of states associated with the facility, wherein states detection and/or recognition of a person, detection of an open door and/or window, detection of fire and/or smoke, detection of an abandoned object, recognition of an activity, and detection of an anomaly state);
cause a display device of a user device to display the graphical model within a user interface (Fig. 30, 31a-31c, para 13, generative or discriminative probabilistic graphical model comprising Bayesian network, Markov random field, fuzzy logic system, neural network, deep neural network, para 93, graphical processing unit and adapted to run machine learning algorithm on graphical processing unit and data storage comprises a database for storing data related to detected states, para 369, acoustic or optic signal displayed on a screen, animating the human being to react); and
cause the user interface to include an indication of the device inspection or maintenance information (para 60, perform inspection tasks, system suitable for autonomous surveillance of area and for detecting and reporting events, para 181, state detection e.g. implemented based on information from a mobile patrolling system that is adapted to patrol an area, like a building to be inspected).
METZLER specifically fails to disclose highlight the one or more devices within a graphical model of a building based on the device location information.
In analogous art, Beale discloses highlight the one or more devices within a graphical model of a building based on the device location information (para 02, methods for building and using a false alarm predicting model to determine whether to alert a user and/or relevant authorities about an alarm signal from a security system, para 15, false alarm predicting model can be managed by the learning module, para 24, learning module can build the false alarm predicting model by recognizing patterns in the historical data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of surveillance sensors adapted for surveillance of facility elements and for generation of surveillance data disclosed by METZLER to use systems and methods for building and using a false alarm predicting model to determine whether to alert a user or relevant authorities about an alarm signal from a security system as taught by Beale to transmit notification signal indicative of alarm signal to devices and response to executing customized response protocol include receiving user input indicating that alarm signal is false alarm or valid alarm or failing to receive user input [Beale, para 0016].
Regarding claim 8, METZLER discloses the building system of claim 7, wherein the device inspection or maintenance information comprises an indication of a latest inspection or maintenance including a date of the latest inspection or maintenance and notes from the latest inspection or maintenance (para 181, based on information a patrol an area, like a building to be inspected, but also based on information from stationary surveillance equipment, like surveillance cameras, various kinds of intrusion sensors (e.g. for light, movements, etc.), combination of stationary and autonomous surveillance of area for automatically detecting and reporting anomalies and/or critical states implemented, para 481, abnormal and a potential security event to be detected and categorized as such by the surveillance system, once detected and classified as resulting in a critical state, needs further inspection and corrective action to be initiated).
Regarding claim 9, METZLER discloses the building system of claim 7, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: navigate a user through the graphical model of the building from an entrance of the building to a device of the one or more devices within the building (para 134, system dynamically learns new critical or non-critical events, and thus false alarms are reduced and only increasingly relevant alerts are pointed, Fig. 30, 31a-31c, para 13, generative or discriminative probabilistic graphical model comprising Bayesian network, Markov random field, fuzzy logic system, neural network, deep neural network, para 93, graphical processing unit and adapted to run machine learning algorithm on graphical processing unit and data storage comprises a database for storing data related to detected states, para 369, acoustic or optic signal displayed on a screen, animating the human being to react).
Regarding claim 10, METZLER discloses the building system of claim 7, wherein the one or more devices are highlighted based on the one or more devices requiring one or more corresponding inspection or maintenance actions (para 481, abnormal and a potential security event to be detected and categorized as such by surveillance system, once detected and classified as resulting in a critical state, needs further inspection and corrective action to be initiated, para 60, perform inspection tasks, system suitable for autonomous surveillance of area and for detecting and reporting events).
Regarding claim 11, METZLER discloses the building system of claim 7, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: detect a fault within a fault detection loop (para 16, state is derived using at least one of detection and/or recognition of persons, detection of open doors and/or windows, detection of fire and/or smoke, detection of abandoned objects, or recognition of activities, para 481, abnormal and a potential security event to be detected and categorized as such by the surveillance system).
METZLER specifically fails to disclose cause the user interface to highlight the fault detection loop within the graphical model; and cause the user interface to include device indications of devices within the fault detection loop capable of triggering the fault.
In analogous art, Beale discloses cause the user interface to highlight the fault detection loop within the graphical model; and cause the user interface to include device indications of devices within the fault detection loop capable of triggering the fault (para 02, methods for building and using a false alarm predicting model to determine whether to alert a user or relevant authorities about alarm signal from a security system, para 15, false alarm predicting model managed by learning module, para 24, learning module can build false alarm predicting model by recognizing patterns in historical data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of surveillance sensors adapted for surveillance of facility elements and for generation of surveillance data disclosed by METZLER to use systems and methods for building and using a false alarm predicting model to determine whether to alert a user or relevant authorities about an alarm signal from a security system as taught by Beale to alert authorities by inserting notification signal indicative of the alarm signal and demographic data associated with alarm signal directly into dispatch system 34. [Beale, para 0016].
Regarding claim 12, METZLER discloses the building system of claim 7, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: cause the display device of the user device to display the graphical model of the building within a campus graphical model of a campus, the campus graphical model including one or more additional graphical models of one or more additional buildings (Fig. 30, 31a-31c, para 13, generative or discriminative probabilistic graphical model comprising Bayesian network, Markov random field, fuzzy logic system, neural network, deep neural network, para 93, graphical processing unit and adapted to run machine learning algorithm on graphical processing unit and data storage comprises a database for storing data related to detected states, para 369, acoustic or optic signal displayed on a screen, animating the human being to react).
Regarding claim 13, METZLER discloses the building system of claim 12, wherein the instructions, when executed by the one or more processors, further cause the one or more processors to: cause the campus graphical model of the campus to include a heat map of devices within the campus or a portion of the campus having pending actions to be completed (Fig. 30, 31a-31c, para 13, generative or discriminative probabilistic graphical model comprising Bayesian network, Markov random field, fuzzy logic system, neural network, deep neural network, para 185, evaluation of specifics of one or more of the surveillance-sensors from same or from another modality, or derived by abstracted virtual state model of building or property in surveillance system, e.g. comprising an electricity state, a lighting state, a heating state, an occupation state, etc).
Regarding claim 14, METZLER discloses the building system of claim 12, wherein the one or more devices are one or more smart handheld fire extinguishers within the campus and the instructions, when executed by the one or more processors, further cause the one or more processors to: cause the campus graphical model of the campus to include an indication that the one or more smart handheld fire extinguishers need to be replaced (para 428, operator 310 classifies 311 the state as either critical 305, and, for example, raises an alert 306 such as calling the police or the fire brigade, or the operator 310 classifies the state as uncritical 307, wherein no action 308 is performe, para 510, classification of a state for being critical or non-critical can be established, but such in many cases also depends on contextual data as well, such as the location in the building, the time, the floor, etc. In case the state is critical an action, e.g. call the police or the fire brigade, etc. has to be performed).
Regarding claim 15, METZLER discloses a building system comprising one or more storage devices storing instructions thereon that, when executed by one or more processors (FIG. 1, building surveillance system 1, para 30, facility security surveillance method for security relevant evaluation of an anomalous state of a facility providing dynamic model of the facility and building information model (BIM), surveilling a plurality and continuously generating surveillance data, analysing of surveillance data and detecting of at least one state),
cause the one or more processors to: ingest information associated with one or more devices within a building, the information comprising at least one of safety information or security information (para 83, generate sensor data, and computing unit comprising a processors and a data storage, para 105, computing unit comprising processors and a data storage, remote computing unit adapted to receive sensor data and to evaluate sensor data in real time);
detect a safety alarm or a security alarm within the building (para 15, determine a probability for a false positive of a classification, trigger acquisition of data, surveillance data, para 306, surveillance system determines if surveilled state pattern as potentially security relevant state is “anomalous” (not a normal state pattern) and determines if such an “anomaly” has to be seen “critical” or not, para 122, monitoring and alarming system, wherein false alarms reduced and alerts are pointed to an operator's attention, para 134, system dynamically learns new critical or non-critical events, and thus false alarms are reduced and only increasingly relevant alerts are pointed);
generate an augmented reality (AR) overlay based on a graphical model of the building (para 523, virtually generated or augmented images are used for training a detection and/or classification unit, e.g. in a supervised learning approach);
overlay the AR overlay over a camera feed of the user's surroundings within a user interface (para 234-235, picture can be augmented to virtually depict a security related anomaly of an object, para 446, training data can e.g. be collected and generate synthetic training data and accomplished by a 3D-model or by augmenting images of one or more persons); and
cause a display device of a user device to display the user interface (Fig. 30, 31a-31c, para 13, generative or discriminative probabilistic graphical model comprising Bayesian network, Markov random field, fuzzy logic system, neural network, deep neural network, para 93, graphical processing unit and adapted to run machine learning algorithm on graphical processing unit and data storage comprises a database for storing data related to detected states, para 369, acoustic or optic signal displayed on a screen, animating the human being to react).
METZLER specifically fails to disclose generate graphical model of the building, a user location of a user, and the safety alarm or the security alarm.
In analogous art, Beale discloses generate graphical model of the building, a user location of a user, and the safety alarm or the security alarm (para 02, methods for building and using a false alarm predicting model to determine whether to alert a user and/or relevant authorities about an alarm signal from a security system, para 15, false alarm predicting model can be managed by the learning module, para 24, learning module can build the false alarm predicting model by recognizing patterns in the historical data).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of surveillance sensors adapted for surveillance of facility elements and for generation of surveillance data disclosed by METZLER to use systems and methods for building and using a false alarm predicting model to determine whether to alert a user or relevant authorities about an alarm signal from a security system as taught by Beale to transmit notification signal indicative of alarm signal to devices and response to executing customized response protocol include receiving user input indicating that alarm signal is false alarm or valid alarm or failing to receive user input [Beale, para 0016].
Regarding claim 19, METZLER discloses the building system of claim 15, wherein the AR overlay includes one or more of historical access information pertaining to a door near the user, an indication of a camera near the user that is capturing video of the user, or a separate camera feed around a corner from the user (para 79, track based on a video stream captured with a camera, i.e. determine a direction, para 357, If its identity could not be unambiguously determined by analyzing first surveying data (an image or video) captured by the first camera 110 (cf. FIG. 2), then identity is verified using second surveillance sensor 111 which is a high resolution camera with enhanced ability of person identification).
Regarding claim 20, METZLER discloses the building system of claim 19, wherein the historical access information includes one or more of an indication of one or more individuals that have entered or exited through the door, an indication of one or more individuals that have been rejected access through the door, or an indication of one or more rejections reasons associated with one or more individuals that have been rejected access through the door (para 438, different monitoring sites may have different locally defined workflows which may consider specific access plans and restriction plans and can optionally be regulated, para 117, visitors may only have restricted access, depending on the type of site, e.g. a construction site or a restricted military site, monitoring site comprise of potentially dangerous areas, e.g, wherein humans or machinery are at risk to be injured or at risk to violate the law, and thus need to be warned when entering these zones).
Allowable Subject Matter
7. Claims 2-3 and 16-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including ALL of the limitations of the base claim and any intervening claims.
Response to Arguments
8. Applicant's arguments filed November 18, 2025 have been fully considered but they are not persuasive.
Double Patenting rejection not withdrawn.
On page 8, lines 2-3, and page 8, lines 10-12, and page 9, lines 13-14, and page 10, lines 13-15, the applicant argues that the reference(s) do not teach or even suggest at least these features as claimed.
The examiner respectfully disagrees and points out that the METZLER teaches as n FIG. 1, building surveillance system 1, and facility security surveillance method for security relevant evaluation of an anomalous state of a facility providing dynamic model of the facility and building information model (BIM), surveilling a plurality and continuously generating surveillance data, analysing of surveillance data and detecting of at least one state [030] and surveillance system determines if surveilled state pattern as potentially security relevant state is “anomalous” (not a normal state pattern) and determines if such an “anomaly” has to be seen “critical” or not [306] and, monitoring and alarming system, wherein false alarms reduced and alerts pointed to an operator's attention [122] and, system dynamically learns new critical or non-critical events, and thus false alarms are reduced and increasingly relevant alerts are pointed) and Fig. 30, 31a-31c [134] and, generative or discriminative probabilistic graphical model comprising Bayesian network, Markov random field, fuzzy logic system, neural network, deep neural network [013] and, graphical processing unit and adapted to run machine learning algorithm on graphical processing unit and data storage comprises a database for storing data related to detected states [093] and, acoustic or optic signal displayed on a screen, animating the human being to react [369], and training data can e.g. be collected and generate synthetic training data and accomplished by a 3D-model or by augmenting images of persons [446], and virtually generated or augmented images are used for training a detection and classification unit, e.g. in a supervised learning approach [523], and graphical output within a graphical, three-dimensional, visualization of the facility model [022] and artificial intelligence computation system trained on training data which is synthetically generated based on a virtual model [225] and virtually generated or augmented images used for training a detection, e.g. in supervised learning approach [523] and
METZLER teaches a methods for building and using a false alarm predicting model to determine whether to alert a user and/or relevant authorities about an alarm signal from a security system [02] and, false alarm predicting model can be managed by the learning module [015] and, learning module can build the false alarm predicting model by recognizing patterns in the historical data [024] .
Thus, METZLER (US 20250209897 A1) (hereinafter METZLER) in view of Beale (US 20230360517 A1) disclose the applicant’s whole invention.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MIRZA ALAM whose telephone number is (469) 295-9286. The examiner can normally be reached on 8:00AM-5:00PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Steven Lim can be reached on 571-270-1210. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MIRZA F ALAM/Primary Examiner, Art Unit 2688