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 .
Response to Arguments
Applicant’s remarks, see pages 5-9 concerning claim 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries 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, 5, 7-11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Amir et al (WO 2022013863 A1), hereinafter Amir in view of Pezeshk et al (US 20210231775 A1), hereinafter Pezeshk
Regarding claim 1, Amir discloses:
an intruder detection system, comprising (Amir, p.1, lines 4-9: Motion sensors are designed to monitor a defined area, which may be outdoors (e.g., entrance to a building, a yard, and the like), and/or indoors (e.g., within a room, in proximity of a door or window, and the like). Motion sensors may be used for security purposes, to detect intruders based on motion in areas in which no motion is expected, for example, an entrance to a home at night.):
a millimeter-wave (mmWave) radar that detects an object and determines a property comprising a size of the object (Amir, p. 9 line 29 – p. 10 line 6 : Preferably, the active reflected wave detector 206 is a radar sensor. The radar sensor 206 may use millimeter wave (mmWave) sensing technology. The radar is, in some embodiments, a continuous-wave radar, such as frequency modulated continuous wave (FMCW) technology. Such a chip with such technology may be, for example, Texas Instruments Inc. part number IWR6843. The radar may operate in microwave frequencies, e.g. in some embodiments a carrier wave in the range of l-lOOGHz (76-8 lGhz or 57-64GHz in some embodiments), and/or radio waves in the 300MHz to 300GHz range, and/or millimeter waves in the 30GHz to 300GHz range. In some embodiments, the radar has a bandwidth of at least 1 GHz. The active reflected wave detector 206 may comprise antennas for both emitting waves and for receiving reflections of the emitted waves, and in some embodiment different antennas may be used for the emitting compared with the receiving) and (further reference p. 12, lines 19-29) Examiner interprets the body portions/parts, torso and head are shapes of different sizes;
an image capture device (Amir, p. 10 line 27 – p. 11 line 4: In embodiments, the CPU 202 is configured to control the camera 208 to capture an image (represented by image data) of the environment. The camera 208 is preferably a visible light camera in that it senses visible light. Alternatively, the camera 208 senses infrared light. One example of a camera which senses infrared light is a night vision camera which operates in the near infrared (e.g. wavelengths in the range 0.7 - 1.4pm) which requires infrared illumination e.g. using infrared LED(s) which is not visible to an intruder. Another example of a camera which senses infrared light is a thermal imaging camera which is passive in that it does not require an illuminator, but rather, senses light in a wavelength range (e.g. a range comprising 7 to 15pm, or 7 to llpm) that includes wavelengths corresponding to blackbody radiation from a living person (around 9.5 pm). The camera 208 may be capable of detecting both visible light and, for night vision, near infrared light.),
comprising an image sensor, that generates a captured image representing a scene under monitoring (Amir -863, p. 10 line 27 – p. 11 line 4);
a processor that determines whether the object is a human according to an output signal of the mmWave radar Figure 2 shows the CPU 202 being connected to a motion sensor 204, an active reflected wave detector 206, and a camera 208. While in the illustrated embodiment the motion sensor 204, active reflected wave detector 206, and the camera 208 are separate from the CPU 202, in other embodiments, at least part of processing aspects of the motion sensor 204 and/or active reflected wave detector 206 and/or camera 208 may be provided by a processor that also provides the CPU 202, and resources of the processor may be shared to provide the functions of the CPU 202 and the processing aspects motion sensor 204 and/or active reflected wave detector 206 and/or camera 208. Similarly, functions of the CPU 202, such as those described herein, may be performed in the motion sensor 204 and/or the active reflected wave detector 206 and/or the camera 208. ) and (p. 9, lines 26-29: The active reflected wave detector 206 may operate in accordance with one of various reflected wave technologies. In operation, the CPU 202 uses the output of the active reflected wave detector 206 to determine the presence of a target object (e.g. human) ;
and a motion sensor that detects motion of the object ( Amir, p. 9 lines 20-25: In embodiments, the CPU 202 is configured to detect motion in the environment based on an output of the motion sensor 204. The motion sensor 204 may be a passive infrared (PIR) sensor. The motion sensor is preferably a PIR sensor, however it could be an active reflected wave sensor, for example radar, that detects motion based on the Doppler effect. For example, the motion sensor 204 may be a radar based motion sensor which detects motion based on the Doppler component of a radar signal:
wherein the image sensor is activated by the millimeter-wave radar only when the object enters a predetermined range within which focal length of the image sensor and a distance between the object and the image sensor are matched to capture a clear image (Amir , p. 9 line 29 – p. 10 line 6; and p. (Amir -863, p. 14 line 29 – p. 15, line 20: n other embodiments, as noted above, the control hub 106 may receive the output signal from the motion sensor 204 and determine that the motion sensor 204 has detected motion in the environment based on the output signal being indicative of detected motion. In these embodiments, in response to the detected motion the control hub 106 may transmit a command to the camera 208 instructing the camera to capture an image of the environment. In these other embodiments, the CPU 202 may receive the image data associated with a captured image from the camera 208. Alternatively the image data may be transmitted from the camera 208 to the control hub 206 which then sends to image data to the CPU 202.
In these other embodiments, the control hub 106 may take into account one or more factors before transmitting the command to activate the active reflected wave detector 206 and the command to the camera 208 instructing the camera to capture an image of the environment. For example, the control hub 106 may be controlled to be in an armed mode or unarmed mode. Thus, in this example the control hub 106 may transmit the command to activate the active reflected wave detector 206 and the command to the camera 208 instructing the camera to capture an image of the environment if the control hub 106 is in an armed mode. However preferably the device 102 knows whether the control hub 106 (or itself) is in an armed mode or unarmed mode and only takes the necessary steps to activate the active reflected wave detector 206 and instruct the camera to capture an image of the environment in the event that the armed mode is active.
In some embodiments, prior to step S406 the camera 208 is in a deactivated state. In the deactivated state the camera 208 may be turned off. Alternatively, in the deactivated state the camera 208 may be turned on but in a low power consumption operating mode (e.g. a sleep mode) whereby the camera 208 is not operable to capture images of the environment. By maintaining the camera 208 in a default state of being deactivated, power is advantageously conserved. In these embodiments, step S406 comprises activating the camera 208 so that it is in an activated state and operable to capture images of the environment. In other words, in the activated state, the camera is powered up and is ready to capture one or more images. It is therefore in a higher power consumption operating mode. Being ready to capture one or more images may comprise having one or more of: a camera aperture setting and a shutter speed/exposure time selected to suit the current ambient conditions (e.g. based on a light intensity measured by a light sensor that is independent of the camera’s image sensor). Being ready to capture one or more images may more particularly comprise having at least the camera aperture setting selected ),
Pezeshk discloses:
and the predetermined range prevents unwanted image from being captured in consideration of privacy wherein the motion sensor activates the mmWave radar only when motion of the object is detected by the motion sensor (Pezeshk, para [0131, and the predetermined range prevents unwanted image from being captured in consideration of privacy wherein the motion sensor activates the mmWave radar only when motion of the object is detected by the motion sensor) and (further reference para [0133] and [0138]).
It would have been obvious to someone in the art prior to the effective filing date of the claimed invention to modify Amir with Pezeshk to incorporate the features of: and the predetermined range prevents unwanted image from being captured in consideration of privacy wherein the motion sensor activates the mmWave radar only when motion of the object is detected by the motion sensor. Both arts disclose systems and methods that comprise millimeter wave antenna array and image processing of objects (i.e. human, people). The modification would render the predictable results of maintenance of privacy during operations and reduction of exposure of sensitive information and reduction of privacy risks.
Regarding claim 2, Amir discloses:
the system of claim 1 (Amir, p.1, lines 4-9),
wherein the property of the object comprise a position of the object (Amir, p. 17, line 25 – p. 18, line 4: Optionally, the CPU 202 may process the accrued measured wave reflection data to perform a determination as to whether the person is in a fall position (i.e. a position that is consistent with them haven fallen) or a non-fall position (indicative that they are, at least temporarily, in a safe state). In some embodiments of the present disclosure the determination that the person is in a fall position or has fallen is used as an indicator that the person may be in need of help, e.g. an intruder has fallen whilst attempting to access a premises. In these embodiments the CPU 202 may generate a fall alert and transmit the fall alert to the control hub 106 for subsequent transmission to one or more of the remote monitoring station 110, the server 112 and the remote personal computing device 114. Additionally or alternatively the CPU 202 may transmit the alert message directly to one or more of the remote monitoring station 110, the server 112 and the remote personal computing device 114. It will be therefore be appreciated that the present invention may also have application beyond the field of security. For example, it may be used as a fall detector to monitor a person at risk of falling) Examiner interprets a fall as a position.
Regarding claim 3, Amir discloses:
the system of claim 1 (Amir, p.1, lines 4-9),
wherein the mmWave radar comprises (Amir, p. 9 line 29 – p. 10 line 6):
a transmitter that produces electromagnetic waves in a millimeter-waveband (Amir, p. 9 line 29 – p. 10 line 6);
an antenna that transmits the electromagnetic waves produced by the transmitter (Amir, p. 9 line 29 – p. 10 line 6);
and a receiver that determines the property of the object according to reflected electromagnetic waves reflected from the object and received by the antenna (Amir, p. 9 line 29 – p. 10 line 6).
4. (Cancelled)
Regarding claim 5, Amir discloses:
the system of claim 1 (Amir, p.1, lines 4-9),
wherein the mmWave radar activates the image capture device to generate the captured image when the object is detected by the mmWave radar (Amir, p. 2, lines 19-26: In some embodiments, the processor is configured to receive the camera image data from a camera, and the at least one operation comprises transmitting a message to a remote device informing the remote device of the capture of said camera image data. The informing of the capturing of the image data may be by implication. For example, in embodiments in which the capturing of an image is triggered by satisfaction of the predetermined condition, it may be that the message indicates that that the predetermined condition has been met, thereby implying that an image has been captured. Additionally or alternatively, the capturing of the image data may be explicitly indicated).
Regarding claim 7, Amir discloses:
the system of claim 1 (Amir, p.1, lines 4-9),
wherein the object is initially determined as a human when the size of the object as determined by the mmWave radar is greater than a predetermined threshold (Amir, p. 12, lines 9-29: Figure 3 illustrates a map of reflections. The size of the point represents the intensity (magnitude) of energy level of the radar reflections (see larger point 306). Different parts or portions of the body reflect the emitted signal (e.g. radar) differently. For example, generally, reflections from areas of the torso 304 are stronger than reflections from the limbs. Each point represents coordinates within a bounding shape for each portion of the body. Each portion can be separately considered and have separate boundaries, e.g. the torso and the head may be designated as different portions. The point cloud can be used as the basis for a calculation of a reference parameter or set of parameters which can be stored instead of or in conjunction with the point cloud data for a reference object (human) for comparison with a parameter or set of parameters derived or calculated from a point cloud for radar detections from an object (human).
When a cluster of measurement points are received from an object in the environment, a location of a particular part/point on the object or a portion of the object, e.g. its centre, may be determined by the CPU 202 from the cluster of measurement point positions having regard to the intensity or magnitude of the reflections (e.g. a centre location comprising an average of the locations of the reflections weighted by their intensity or magnitude). As illustrated in figure 3, the reference body has a point cloud from which its centre has been calculated and represented by the location 308, represented by the star shape. In this embodiment, the torso 304 of the body is separately identified from the body and the centre of that portion of the body is indicated. In alternative embodiments, the body can be treated as a whole or a centre can be determined for each of more than one body part e.g. the torso and the head, for separate comparisons with centres of corresponding portions of a scanned body).
Regarding claim 8, Amir discloses:
the system of claim 7 (Amir, p.1, lines 4-9),
wherein the processor further performs a subsequent image operation on the captured image after the object is initially determined as a human (Amir, p. 21 line 31-p. 22 line 9: In the process 500, before the CPU 202 controls the camera 208 to capture an image, the process 500 proceeds to step S506 where the CPU 202 processes accrued measured wave reflection data to determine whether a predetermined condition is met. Step S506 may correspond to step S410. Examples of how step S410 (and thus step S506) may be implemented have been described above and are therefore not repeated. However, in other embodiments, the predetermined condition may be that it is determined that the person is in a fall position or has fallen.
If, at step S506, the CPU 202 determines that the predetermined condition is met, the CPU 202 determines that an intruder is present in the monitored environment and the process 500 proceeds to step S508 where the CPU 202 controls the camera 208 to capture an image of the environment. Step S508 may correspond to (e.g. involve the same actions as) step S406 of figure 4)
thereby confirming an intrusion by a human (Amir , p. 21 line 31-p. 22 line 9).
Regarding claim 9, Amir discloses:
the system of claim 8 (Amir, p.1, lines 4-9),
wherein the subsequent image operation comprises manual visual verification or image processing (Amir, p. 18 line 26 – p. 19 line 3: In response to determining at step S410 that the predetermined condition is met, the process 400 may proceed to step S412 where the image data representing the captured image is processed locally on the device 102 to verify that the predetermined condition is met. This image processing may be performed by the image processing module 212 on the CPU 202 (as shown in Figure 2). Alternatively the CPU 202 may supply to the image data to another local processor comprising the image processing module 212 to perform the image processing. It can be seen that the power intensive image processing is only performed if both the motion sensor 204 has detected motion and the predetermined condition is met based on processing accrued measured wave reflection data from the active reflected wave detector 206).
Regarding claim 10, Amir discloses:
the system of claim 9 (Amir, p.1, lines 4-9),
Pezeshk discloses:
wherein the image processing is performed based on artificial intelligent (AI) filtering (Pezeshk, para [0055], A neural network, sometimes referred to as an artificial neural network, is a computing system/apparatus based on consideration of biological neural networks of animal brains. Such systems/apparatus progressively improve performance, which is referred to as learning, to perform tasks, typically without task-specific programming. For example, in image recognition, a neural network may be taught to identify images that contain an object by analyzing example images that have been tagged with a name for the object and, having learnt the object and name, may use the analytic results to identify the object in untagged images. A neural network is based on a collection of connected units called neurons, where each connection, called a synapse, between neurons can transmit a unidirectional signal with an activating strength that varies with the strength of the connection. The receiving neuron can activate and propagate a signal to downstream neurons connected to it, typically based on whether the combined incoming signals, which are from potentially many transmitting neurons, are of sufficient strength, where strength is a parameter)
It would have been obvious to someone in the art prior to the effective filing date of the claimed invention to modify Amir with Pezeshk to incorporate the features of: wherein the image processing is performed based on artificial intelligent (AI) filtering. Both arts disclose systems and methods that comprise millimeter wave antenna array and image processing of objects such as humans; however, Amir fails to disclose: wherein the image processing is performed based on artificial intelligent (AI) filtering as disclosed by Pezeshk. The modification would render the predictable results of improved detection accuracy, real-time object recognition, and improved behavioral analysis and threat prioritization.
Regarding claim 11, Amir discloses:
the system of claim 9 (Amir, p.1, lines 4-9),
wherein the image processing comprises analyzing shape and size of the object (Amir, p.12 lines 19-29: When a cluster of measurement points are received from an object in the environment, a location of a particular part/point on the object or a portion of the object, e.g. its centre, may be determined by the CPU 202 from the cluster of measurement point positions having regard to the intensity or magnitude of the reflections (e.g. a centre location comprising an average of the locations of the reflections weighted by their intensity or magnitude). As illustrated in figure 3, the reference body has a point cloud from which its centre has been calculated and represented by the location 308, represented by the star shape. In this embodiment, the torso 304 of the body is separately identified from the body and the centre of that portion of the body is indicated. In alternative embodiments, the body can be treated as a whole or a centre can be determined for each of more than one body part e.g. the torso and the head, for separate comparisons with centres of corresponding portions of a scanned body.),
analyzing color and texture of the object,
or using a pattern recognition technique to identify specific features of the object (Amir, p. 17, line 25 – p. 18, line 4) Examiner interprets the fallen state of the intruder as a recognition of a pattern i.e. falling.
Regarding claim 13, Amir, discloses:
the system of claim 1 (Amir, p.1, lines 4-9),
wherein the motion sensor comprises a passive infrared (PIR) motion sensor (Amir, p. 9 lines 20-25).
References Cited But Not Relied Upon
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure as thus:
Velipasalar et al US 20200089967 A1 privacy preserving sensor and low power (Abstract, A low-cost, low-power, stand-alone sensor platform having a visible-range camera sensor, a thermopile array, a microphone, a motion sensor, and a microprocessor that is configured to perform occupancy detection and counting while preserving the privacy of occupants. The platform is programmed to extract shape/texture from images in spatial domain; motion from video in time domain; and audio features in frequency domain. Embedded binarized neural networks are used for efficient object of interest detection. The platform is also programmed with advanced fusion algorithms for multiple sensor modalities addressing dependent sensor observations. The platform may be deployed for (i) residential use in detecting occupants for autonomously controlling building systems, such as HVAC and lighting systems, to provide energy savings, (ii) security and surveillance, such as to detect loitering and surveil places of interest, (iii) analyzing customer behavior and flows, (iv) identifying high performing stores by retailers.)
Russel US 20190035128 A1 (para [0005], It would therefore be desirable to provide a UAV that allows for enhanced privacy and/or security and/or the like.) and (para [0012], In examples described herein, an image processor is provided (for example in the UAV) that receives the captured image data output by the camera. The image processor generates image data corresponding to a second, different representation of the scene. A part of the scene that is visible through the window in the first representation of the scene is represented differently in the first and second representations of the scene, for example to enhance privacy. The image processor may generate the image data corresponding to the second representation of the scene by modifying the captured image data corresponding to the first representation of the scene (for example using a pixilation technique) and/or by combining data (for example overlay data) with the captured image data corresponding to the first representation of the scene
Trundle et al US 9153111 B2 discloses an intrusion security system
Kempshall et al US 20210149047 A1 discloses a detection system that uses mmWave wherein sensors are activated upon detection, [0019] Metal and ceramic objects scatter millimeter wave energy more strongly than most clothing and human body surfaces, so the system 100 will identify areas of interest based on intensity of scattering. The short wavelength of the source signals 110 allows high-resolution imaging so that an automated detection algorithm can use the size and the shape of the highly reflective regions of the image to identify potential weapons. Other sensors (e.g., visible or infrared cameras) can also be accurately directed to further examine target regions to aid in identification. For example, when a suspicious object 114 is detected based on the three-dimensional image generated using the detected signal data from the receivers 108, additional sensors may be activated and directed to the location of the suspicious object 114 for further image detection
Tilkin et al US10937290B2 discloses PIR detection, perimeter intrusion detector, microwave, privacy protection via pixilation/blurring (36) The non-photorealistic representation of the scene can be generated from the received video, generated from an earlier image of the scene, or in some other manner. Generating the non-photorealistic representation of the scene from the received video can include processing the received video to adulterate or obscure the realism of the video, e.g. by performing one or a combination of the following image processing steps: blurring, pixelating, modifying one or more attributes of each of the one or more video frames, e.g. resolution, gradient, brightness, contrast, saturation, sharpness, hue or similar thereof, masking, image distortion, merging frames, temporal or spatial averaging, compressing, replacing at least a portion or an entire video frame with another predetermined or a randomly generated image, and so on. The aim being to generate a ‘non-photorealistic’ image in which there is less correlation between the scene being monitored and the second video generated (in comparison to the amount of correlation between an original video received and the real scene) in order to prevent at least some details of the scene being visually identifiable by a person looking at the second video. The details may include characteristics of one or more objects contained in the video, for example, an animal, a person, a building structure, a vehicle, or similar thereof.
Zhang et al US 20220381898 A1 discloses mmWave sensing of humans wherein privacy is considered from image capturing devices
Matusek et al US10915660B2 discloses a method and apparatus for using video analytics to detect regions for privacy protection with images from moving cameras
Goldstein et al US 20220057519 A1 discloses an automate threat detection and deterrence apparatus wherein privacy is preserved from the imaging devices
Kempshall et al US 20210149047 A1 discloses a standoff detection system that comprises object detection, mmWave energy, target discrimination and obfuscation for privacy preservation
Watanabe et al US 20200185804 A1 discloses subject monitoring via mmWave radar with image sensors i.e. cameras and sensing abilities to protect privacy
Zhang et al US 20220381898 A1 discloses image sensors (cameras) with mmWave wherein size, shape, and position are reflected within the signal [0059]
Goldstein et al US 20220057519 A1 discloses an automated threat detection and deterrent measures
Yoda US 8305448 B2 selective privacy protection for imaged matter via privacy mask processing
Salgar et al US 8576282 B2 security system with operator side privacy zones and a privacy mask generator
Langhammer et al US11049377B2 discloses an alarm dependent video surveillance wherein actuation until object enters area privacy filter, making objects irrecognizable (17) The step of “making objects in the area of surveillance irrecognizable or invisible in the captured video images” functions as a “privacy filter”. No personal data are obtained from objects in the area of surveillance, if the objects are irrecognizable or invisible in the captured video images. In particular, as the objects are not visible and/or recognizable in the captured video images, no personal data of an individual are captured, if one or more of the objects are individuals. Hence, an identification of the individuals is not possible. The right of privacy is preserved although the video camera is operating.
Regani et al US 11953618 B2 discloses wireless motion recognition wherein characteristics include location, position, state, size, length, width, height, shape, curve, gesture, limbs, posture, etc.
Goldstein et al US 11747480 B2 discloses an automated threat detection and deterrence apparatus that analyze the size of objects
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 nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIMBERLY JENKINS whose telephone number is (571)272-0404. The examiner can normally be reached Monday - Friday 8a-5p EST.
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, Vladimir Magloire can be reached at 517.270.5144. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KIMBERLY JENKINS/Examiner, Art Unit 3648
/VLADIMIR MAGLOIRE/Supervisory Patent Examiner, Art Unit 3648
Prosecution Insights