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
Claims 1-11 are presented for examination.
Claim Rejections - 35 USC § 103
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 may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
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 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.
Claims 1-3 are rejected under 35 USC 103 as being unpatentable over Englund U.S. 2020/0191613 in view of Azimi-Sadjadi et al., U.S. 2012/0300587 (hereinafter 587).
On claim 1, Englund cites except as underlined:
A system for substation security comprising:
a distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) system;
[0062] The disclosed system and method make use of fibre optic distributed acoustic sensing to provide spatial and temporal surveillance and monitoring data within a geographical area, such as a city, utilising one or more optical fibres distributed across the geographical area. Such a sensing technique relies on the occurrence of a nearby acoustic event causing a corresponding local perturbation of refractive index along an optical fibre. The required proximity of the acoustic event depends on noise floor of the sensing equipment, the background noise, and the acoustic properties of the medium or media between the acoustic event and the optical fibre. Due to the perturbed refractive index, an optical interrogation signal transmitted along an optical fibre and then back-scattered in a distributed manner (e.g. via Rayleigh scattering or other similar scattering phenomena) along the length of the fibre will manifest in fluctuations (e.g. in intensity and/or phase) over time in the reflected light. The magnitude of the fluctuations relates to the severity or proximity of the acoustic disturbance. The timing of the fluctuations along the distributed back-scattering time scale relates to the location of the acoustic event.
(the above passage describes a distributed fiber optic network)
[0066] In one example, a system 100 for use in distributed acoustic sensing (DAS) is illustrated in FIG. 1. The DAS system 100 includes a coherent optical time-domain reflectometer (C-OTDR) 102. The C-OTDR 102 includes a light source 104 to emit an optical interrogation field 106 in the form of a short optical pulse to be sent into each of optical fibres 105A, 105B and 105C. The optical fibres 105A, 105B and 105C are distributed across a geographical area 107. The C-OTDR 102 includes a photodetector 108 configured to detect the reflected light 110 scattered in a distributed manner and produce a corresponding electrical signal 112 with an amplitude proportional to the reflected optical intensity resolved over time. The time scale may be translated to a distance scale relative to the photodetector 108. An inset in FIG. 1 illustrates a schematic plot of such signal amplitude over distance at one particular instant. The DAS system 100 also includes a processing unit 114, within or separate from the C-OTDR 102, configured to process the acoustic fluctuations 116 in the electrical signal 112.
the substation security system including circuitry configured to:
Abstract: The trajectory of a bullet is determined, providing insights into the direction and potential target within the substation. Al algorithms discern between various acoustic events and provide identification of genuine threats. Upon detecting a potential gunshot, our system automatically correlates related acoustic events, such as the noise of a nearby vehicle, offering context and aiding in threat assessment. Our AI-enhanced system evaluates acoustic signals to determine real-time equipment damage resulting from gunshots, ensuring immediate remedial actions and anticipate potential future incidents.
detect and localize acoustic events including gunshots from DAS data using time difference of arrival (TDOA)
[0061] The surveillance data can relate to real-time acoustic data for monitoring targets. Alternatively or additionally, the surveillance data relates to historic acoustic data for later retrieval and searching. In general, “targets” include any acoustic objects that vibrate and therefore generate detectable acoustic signals, such as vehicles (generating tyre/engine noise), pedestrians (generating footsteps), trains (generating rail track noise), building operations (generating operating noise), and road, track or infrastructure works (generating operating noise). They also include events caused by targets, such as car crashes, gunshots caused by a handgun or an explosion caused by explosives (generating high-pressure sound waves and reverberation).
[0070] This configuration permits determination of an acoustic signal (amplitude, frequency and phase) at every distance along the fibre-optic sensing cable. In one embodiment, the photodetector/receiver records the arrival times of the pulses of reflected light in order to determine the location and therefore the channel where the reflected light was generated along the fibre-optic sensing cable. This phased array processing may permit improved signal-to-noise ratios in order to obtain improved detection of an acoustic source, as well as the properties of the acoustic source.
and angle of arrival (AOA) methodologies.
[0097] With the grid of fibre paths and substantially overlapping sensing range described in this disclosure, multiple phased array beams may be formed with subsets of sensor channels from the total sensor array formed over the length of optical fibre interrogates. This plurality of beams may have different spatial positions (ie. which subset of sensors from the total sensor array are selected corresponding to a different geographical location in the system), angular orientation (which angle or angles relative to the local length axis of the fiber) and/or directivity (aspect ratio of the sensing beams—ie. how sharp or obtuse are the beam spatial shapes) properties around the system to achieve higher level sensing functions in the system that include long range detection, localisation, classification and tracking of acoustic sources in a 2D or 3D coordinate system.
Regarding the excepted: and angle of arrival (AOA) methodologies, Englund, as above, disclosed an embodiment wherein angular orientation (which angle or angles relative to the local length axis of the fiber) and/or directivity (aspect ratio of the sensing beams—ie. how sharp or obtuse are the beam spatial shapes) properties around the system are used to achieve higher level sensing functions.
However, Englund did not disclose the use of angles in determining the location of a disturbance.
In the same art of gunshot detection systems, 587 discloses:
[0003] Guns, including firearms and artillery, generally generate a muzzle blast when the gun is fired. The muzzle blast propagates spherically outwardly from the muzzle of the gun. Some prior known systems use only the muzzle blast to attempt to locate the origin of the gunshot. Such systems can use acoustic sensors and triangulation methods to find the origin of the gunshot.
[0009] Systems and methods that use several closely spaced sensors may be placed in a disadvantaged location and receive only reflected and/or corrupted signals. A distributed network of sensors is more likely to receive clean, direct acoustic signals at enough nodes to reliably calculate the origin and trajectory of a gunshot projectile. Additionally, array-based systems and methods require precise orientation of the sensor array in order to find the angle of arrival of the shock wave. The array-based systems and methods require proper calibration in order to provide an accurate angle of arrival.
In other words, 587 discloses a way to improve a detected signal using angle of arrival techniques a system employs a distributed network of sensors, as in what is disclosed in Englund.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Englund the angle of arrival determination feature disclosed in 587. As disclosed in 587, an improvement in detection of an acoustic event can be had if the system detecting the event includes a distributed sensor system. One of ordinary skill would have employed 587’s angle of arrival feature into Englund’s gunshot detection system employing a distributed acoustic sensing system to realize an improved gunshot detection system as described in 587.
On claim 2, Englund and 587 cites:
The system of claim 1 configured to triangulate the gunshots origin by determining differences in arrival times and angles of the gunshot acoustic events detected by the DAS at a plurality of DAS sensor fiber locations.
[0003] Guns, including firearms and artillery, generally generate a muzzle blast when the gun is fired. The muzzle blast propagates spherically outwardly from the muzzle of the gun. Some prior known systems use only the muzzle blast to attempt to locate the origin of the gunshot. Such systems can use acoustic sensors and triangulation methods to find the origin of the gunshot.
On claim 3, Englund cites:
The system of claim 2 configured to determine real-time bullet trajectory of the gunshot acoustic events detected.
Abstract: The trajectory of a bullet is determined, providing insights into the direction and potential target within the substation. Al algorithms discern between various acoustic events and provide identification of genuine threats. Upon detecting a potential gunshot, our system automatically correlates related acoustic events, such as the noise of a nearby vehicle, offering context and aiding in threat assessment. Our AI-enhanced system evaluates acoustic signals to determine real-time equipment damage resulting from gunshots, ensuring immediate remedial actions and anticipate potential future incidents.
Claim 4 is rejected under 35 USC 103 as being unpatentable over Englund U.S. 2020/0191613 in view of Azimi-Sadjadi et al., U.S. 2012/0300587 (hereinaffer 587) and Frimpong et al., U.S. 2017/0274419 (hereinafter 419) and Gould et al., WO 2011/121338A1.
On claim 4, Englund cites except as underlined:
The system of claim 3 configured to determine the real-time bullet trajectory including bullet direction and speed from the gunshot acoustic events frequency change.
In the rejection of claim 3, Englund disclosed an embodiment wherein
Abstract: The trajectory of a bullet is determined, providing insights into the direction and potential target within the substation. Al algorithms discern between various acoustic events and provide identification of genuine threats. Upon detecting a potential gunshot, our system automatically correlates related acoustic events, such as the noise of a nearby vehicle, offering context and aiding in threat assessment. Our AI-enhanced system evaluates acoustic signals to determine real-time equipment damage resulting from gunshots, ensuring immediate remedial actions and anticipate potential future incidents.
Englund did not disclose this feature.
In the similar art of transformer protection, 419 cites:
[0256] The second category solution uses multiple sensors and more complex algorithms to provide actionable information, such as the shooter direction and location, as well as bullet trajectory, speed, caliber, and number of shots. When an impact is detected in real-time, an alarm signal may be transmitted to the control station and a substation's camera may be then directed to the location of interest.
Furthermore, Gould cites:
Page 14, lines 16-22: In this embodiment the processor 3 compares the frequency of the microwave signal sent by the transmission antennas of the radar antenna array 4 to that reflected from the bullet 10 and received by the radar antenna array 4, allowing for the direct and highly accurate measurement of target velocity component in the direction of the beam. In this way, the processor 3 uses the Doppler Effect of the returned microwave signals from the bullet to determine its radial velocity.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention. One of ordinary skill, apprised of the known measuring techniques disclosed in Gould and 419 would have provided an embodiment meeting the claimed invention.
Claim 5 is rejected under 35 USC 103 as being unpatentable over Englund U.S. 2020/0191613 in view of Azimi-Sadjadi et al., U.S. 2012/0300587 (hereinaffer 587) and Frimpong et al., U.S. 2017/0274419 (hereinafter 419) and Gould et al., WO 2011/121338A1 and Berger, WO 2006/110630.
On claim 5, Englund cites except as underlined:
The system of claim 1 further comprising one or more convolutional neural networks (CNN), the system configured to distinguish gunshot acoustic events from other, non-gunshot acoustic events.
In the rejection of claim 4, Englund cited:
Abstract: The trajectory of a bullet is determined, providing insights into the direction and potential target within the substation. Al algorithms discern between various acoustic events and provide identification of genuine threats. Upon detecting a potential gunshot, our system automatically correlates related acoustic events, such as the noise of a nearby vehicle, offering context and aiding in threat assessment. Our AI-enhanced system evaluates acoustic signals to determine real-time equipment damage resulting from gunshots, ensuring immediate remedial actions and anticipate potential future incidents.
Englund did not disclose using neural networks to classify the type of gunshot detected in the system. In the same art of acoustic event locating, Berger cites:
Page 1, lines 4-12: The present invention relates to a system and a method for identifying and locating an acoustic event and providing evidence as to the source of that event. The invention has particular utility as a system for identifying an explosive event, e.g., a gunshot, at a remote location and determining a location of the gunshot, time the gunshot occurred, and identity of the source of the gunshot, e.g., weapon type, weapon caliber, shooter, etc., using a neural network and a triangulation method which runs on a Digital Signal Processor which displays the results of the data acquisition and calculations in real time, and will be described in connection with such utility, although other utilities are contemplated.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Englund the acoustic event detection system of Berger such that the claimed invention is realized. Berger uses a known embodiment of neural networks to determine the type of weapon, caliber, and the identity of an event such that the claimed invention is realized. One of ordinary skill, apprised of Berger’s embodiment, would have included this feature into Englund as a means to determine the extend of the type of weapons and ammunition used in the system.
On claim 6, Englund cites except as underlined:
The system of claim 5 configured to distinguish a type and extent of damage to the substation resulting from bullet impacts.
Englund discloses:
Abstract: Our AI-enhanced system evaluates acoustic signals to determine real-time equipment damage resulting from gunshots, ensuring immediate remedial actions and anticipate potential future incidents.
Englund doesn’t cite the excepted claim limitations. In the related art of transformer protection, 419 cites:
[0250] An option that can address some of the shortcomings of using only the accelerometer sensors or only the acoustic sensors would be to include one of each. While an accelerometer may not always differentiate between a firearm and a different type of impact, the combination of an accelerometer and an acoustic sensor may be used to pick up also the pressure levels and identify a bullet impact. While an acoustic sensor might capture events that are not associated with the inductive device but are nearby, cross-referencing with the accelerometer can reveal a simultaneous vibration signal received from the inductive device.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to modify Englund’s gunshot tracking system with 419’s bullet damage assessment feature to realize an embodiment meeting the claimed limitations. 419 discloses a combination of an acoustic sensor with pressure level measurement to assess bullet impact. The cited pressure levels is taken to mean the amount of bullet pressure one can experience in the environment. For example a 22 Long Rifle bullet will likely produce less pressure than a 357 Magnum bullet due to their respective energy levels. Accordingly, one of ordinary skill, apprise of the types of bullet impacts being experiences, would assess the damage from one identified bullet to be different from another.
Claim 7 is rejected under 35 USC 103 as being unpatentable over Englund U.S. 2020/0191613 in view of Azimi-Sadjadi et al., U.S. 2012/0300587 (hereinafter 587) and Frimpong et al., U.S. 2017/0274419 (hereinafter 419) and Gould et al., WO 2011/121338A1 and Berger, WO 2006/110630 and Fisher et al., U.S. 2008/0219100 and Hermann et al., U.S. 2015/0177363.
On claim 7, Englund cites except as underlined:
The system of claim 6 configured to distinguish the type and extend of damage to the substation resulting from bullet impacts includes analyzing post-gunshot acoustic signals comprising reflections, vibrations, and resonance patterns.
Regarding the excepted “vibrations,” Englund, while disclosing
Abstract: Our AI-enhanced system evaluates acoustic signals to determine real-time equipment damage resulting from gunshots, ensuring immediate remedial actions and anticipate potential future incidents.
Englund doesn’t cite the excepted claim limitations. In the related art of transformer protection, 419 cites:
[0177] The inductive device 10 is equipped with vibration sensors for sensing impact and an alarm for notifying personnel when the transformer 10 receives a shock or vibration, such as from a ballistic projectile. If the shock, vibration or noise level is above the threshold for shocks or vibrations experienced during normal operation of the inductive device 10, a safety mode is activated.
And
[0054] FIG. 40 is a plot of acceleration versus time for the bullet impact of trial 12 as measured by the raw vibration and RMS sensors
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Englund the ability to measure vibrations issuing from a gunshot’s impact on a target. One of ordinary skill would have included this feature as another way to determine the amount of damage upon an impacted target.
Regarding the excepted “resonance,” Englund, while disclosing
Abstract: Our AI-enhanced system evaluates acoustic signals to determine real-time equipment damage resulting from gunshots, ensuring immediate remedial actions and anticipate potential future incidents.
Englund doesn’t cite the excepted claim limitations. In the related art of gunshot location, Fisher cites:
[0060] With still further reference to FIG. 13, the spectral content of gunshot 200 can be found by performing a transformation from the time domain, as represented by graph 200 to the frequency domain, as represented by graph 500, typically through a Fourier transform. As can be seen in FIG. 13, the spectral content of gunshot 200 shows a noise floor 504 extending from the lower end of the audible spectrum and tapering off somewhere above 1 kilohertz. Of particular significance is the single predominant spike 502 at approximately 240 Hertz. Typically, significant periodic information would be indicative of a resonance, likely from the frame of the gun, the resonance of the barrel cavity, or other like feature of the gun.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Englund the ability to measure resonance issuing from a detected gunshot. One of ordinary skill would have included this feature as another way to determine the type of weapon involved.
Regarding the excepted “reflections,” Englund, while disclosing
Abstract: Our AI-enhanced system evaluates acoustic signals to determine real-time equipment damage resulting from gunshots, ensuring immediate remedial actions and anticipate potential future incidents.
Englund doesn’t disclose embodiments involving reflections.
Hermann cites:
[0015] Moreover, as mentioned above, and as is required when using data 115 from Type A vehicles 101, the computer 105 and/or the server 125 may utilize a time of day at which a gunshot event was recognized to further improve accuracy of the location 195 approximation. For example, a time signature stamp may be obtained from a shared clock such as that received from GPS satellites to ensure each vehicle 101 stamps the time to a common reference. Further, the accuracy of the time stamp should have a resolution (e., 10 milliseconds) sufficient to map out audio reflections from the multiple locations from which a gunshot is captured to allow triangulation.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Englund the ability to measure reflections issuing from a detected gunshot. One of ordinary skill would have included this feature as another way to locate the source of the gunshot.
Claims 8 and 10 are rejected under 35 USC 103 as being unpatentable over Englund U.S. 2020/0191613 in view of Azimi-Sadjadi et al., U.S. 2012/0300587 (hereinaffer 587) and Frimpong et al., U.S. 2017/0274419 (hereinafter 419) and Gould et al., WO 2011/121338A1 and Berger, WO 2006/110630 and Onofrio et al., U.S. 12,566,238.
On claim 8, Englund cites except as indicated:
The system of claim 7 configured to provide gunshot event correlation to acoustic events occurring before and after the gunshot event.
Englund cites: Abstract: Our AI-enhanced system evaluates acoustic signals to determine real-time equipment damage resulting from gunshots, ensuring immediate remedial actions and anticipate potential future incidents.
Englund doesn’t disclose embodiments involving event correlation with gunshots.
In the same art of gunshot detection and locating, Onofrio cites:
Col. 3, lines 12-21 cites: The acoustic information can be used to identify a high-intensity gunshot sound, and to correlate, using the gunshot sensor device, the high-intensity gunshot sound to the infrared information that was collected. The collected IR information can be buffered. The correlating can include establishing a temporal correspondence between the gunshot sound and an infrared event that occurred in time before the gunshot sound. The temporal correspondence can identify the number of milliseconds (time) between the events.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Englund the gunshot correlation feature of Onofrio such that the claimed invention is realized. Onofrio discloses a known embodiment for matching an IR event with a gunshot and one of ordinary skill would have used such a feature to match audio to visual occurrences.
On claim 10, Englund and 587 cites:
The system of claim 8 wherein the angle of arrival is the angle at which an acoustic signal arrives at a sensor and is defined by an azimuth angle and an elevation angle. In the rejection of claim 1, 587 cited:
[0003] Guns, including firearms and artillery, generally generate a muzzle blast when the gun is fired. The muzzle blast propagates spherically outwardly from the muzzle of the gun. Some prior known systems use only the muzzle blast to attempt to locate the origin of the gunshot. Such systems can use acoustic sensors and triangulation methods to find the origin of the gunshot.
[0009] Systems and methods that use several closely spaced sensors may be placed in a disadvantaged location and receive only reflected and/or corrupted signals. A distributed network of sensors is more likely to receive clean, direct acoustic signals at enough nodes to reliably calculate the origin and trajectory of a gunshot projectile. Additionally, array-based systems and methods require precise orientation of the sensor array in order to find the angle of arrival of the shock wave. The array-based systems and methods require proper calibration in order to provide an accurate angle of arrival.
In order to obtain data from angle-of-arrival measurements, it is inherent that an determining the location of a signal, two things must be determined: the location or bearing of the signal and the height at which a receiver’s antenna is pointed at the signal. These are automatic requirements for determining the direction of something. The only parameter needed is a reference angle and reference height, which is inherent if one needs to define an angle and a height.
Claim 9 is rejected under 35 USC 103 as being unpatentable over Englund U.S. 2020/0191613 in view of Azimi-Sadjadi et al., U.S. 2012/0300587 (hereinaffer 587) and Frimpong et al., U.S. 2017/0274419 (hereinafter 419) and Gould et al., WO 2011/121338A1 and Berger, WO 2006/110630 and Onofrio et al., U.S. 12,566,238 and Jung et al., U.S. 2009/0319551.
On claim 9, Englund cites except:
The system of claim 8 wherein the acoustic events occurring before and after the gunshot event include vehicle noises and voices.
Englund cites: Abstract: Our AI-enhanced system evaluates acoustic signals to determine real-time equipment damage resulting from gunshots, ensuring immediate remedial actions and anticipate potential future incidents.
Englund doesn’t disclose embodiments involving event correlation with gunshots
In the same art of event detection, Jung discloses:
[0097] The event-data storage program automatically searches each sensor data set for sensor data having representative features correlating to a gunshot, siren, tire screech, or loud voices using the selected pattern recognition criteria. If sensor data correlating to a representative feature of a gunshot, siren, tire screech, and loud voices is found, the program stores the correlated sensor data in a retained data storage.
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Englund the event-data correlation feature disclosed in Jung such that the claimed invention is realized. One of ordinary skill would have included such a feature to assemble a change of events leading up to and determining an aftermath of a shooting.
Claim 11 is rejected under 35 USC 103 as being unpatentable over Englund U.S. 2020/0191613 in view of Azimi-Sadjadi et al., U.S. 2012/0300587 (hereinaffer 587) and Frimpong et al., U.S. 2017/0274419 (hereinafter 419) and Gould et al., WO 2011/121338A1 and Berger, WO 2006/110630 and Onofrio et al., U.S. 12,566,238 and Holland et al., U.S. 2009/0045939.
On claim 11, Englund and 587 cites except as underlined:
The system of claim 8 wherein TDOA hyperbolic equations and AOA directional vectors are used to triangulate a 3D position of gunshot.
587 cites:
[0003] Guns, including firearms and artillery, generally generate a muzzle blast when the gun is fired. The muzzle blast propagates spherically outwardly from the muzzle of the gun. Some prior known systems use only the muzzle blast to attempt to locate the origin of the gunshot. Such systems can use acoustic sensors and triangulation methods to find the origin of the gunshot. Other systems that use only the muzzle blast for locating the origin of the gunshot, record the Time of Arrival (ToA) of the wavefront of the muzzle blast at a plurality of sensors in known locations. These systems can perform trilateration or multilateration using the ToA or the Time Difference of Arrival (TDoA) of the acoustic wavefront of the muzzle blast at the sensors to locate the origin of the gunshot.
Figure 4 and [0048] After the substep of constructing a feature vector 43, the next step at each sensor node 15 is transmitting the observed TOAs for each segment and the aggregated feature vector 44. Alternatively, a simple classification system such as minimum distance classifier can be implemented on the processor 22 on each sensor node 15 to determine the type of each transient. In this case, a class label, instead of feature vector, is transmitted along with the observed ToAs.
However, neither Englund nor 587 disclose the excepted claim limitations.
In the same art of wireless device tracking, Holland cites:
[0049] Many different methods for determining location can be used. For example, if the devices having known locations densely populate an area, a quick determination can be made that a new device is within a certain radius (or other measure of proximity) of a nearby device sensing the new device. If multiple devices sense the new device, a sort routine can be used by a location engine to estimate the device nearest the new device and the new device can be associated with a space relating to the estimated nearest device. Yet further, methods for determining the location of a device using wireless devices in the space can range from a triangulation method, a multilateration method, a hyperbolic positioning method, a processes based on the time difference of arrival (TDOA), a trilateration method
It would have been obvious to one of ordinary skill before the effective filing date of the claimed invention to include into Englund and 587 the features disclosed in Holland such that the claimed invention is realized. Holland represents a known usage of hyperbolic positioning and one of ordinary skill, apprised of this known locating feature, would have incorporated this teaching into Englund and 587 as an alternative means of tracking something.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAL EUSTAQUIO whose telephone number is (571)270-7229. The examiner can normally be reached on 8am-5pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Brian Zimmerman, can be reached at (571) 272-3059. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application lnformation Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAlR only. For more information about the PAlR system, see http:/lpair-direct.uspto.gov. Should you have questions on access to the Private PAlR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-91 99 (IN USA OR CANADA) or 571-272-1000.
/CAL J EUSTAQUIO/Examiner, Art Unit 2686
/BRIAN A ZIMMERMAN/Supervisory Patent Examiner, Art Unit 2686
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