DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim 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.
Claim(s) 1, 6, 9, 13, 15 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US 20250316002 A1), hereinafter as Deng, in view of Toshio et al. (US 20200223352 A1), hereinafter as Toshio.
Regarding claim 1, Deng teaches A system (Deng paragraph [0013] “A host system in the vehicle communicates with the camera to acquire these image frames or videos, which are represented by Img below. According to an example of the present disclosure, the host system comprises a figure changing control device. As shown in FIG. 1”), comprising: a processor; and a memory storing machine-readable instructions that, when executed by the processor, cause the processor to (Deng paragraph [0058] “The machine-readable instruction, when executed by a processor, causes the processor to execute any of the methods disclosed above in the description.”):
extract physical characteristics of an occupant of a vehicle from a vehicle-captured image of the occupant (Deng paragraph [0013] “A vehicle cabin is typically provided with an in-vehicle camera therein, which is configured to acquire images of an in-vehicle scene, wherein the images comprise image frames or a video stream of the driver and other passengers……the image analysis module 101 further performs analysis to determine figure features FigureFeature of the person”);
render a base digital avatar of the occupant based on extracted physical characteristics of the occupant (Deng paragraph [0017] “The figure image generation module 102 is configured to change the figure of the in-vehicle digital assistant Avatar, so that the changed figure of the digital assistant Avatar matches figure features of the analyzed person. For example, for the driver in the example above, who is wearing the sunglasses and the black leather jacket, the figure image generation module 102 also wears a baseball cap, sunglasses and a black top on Avatar when generating the digital assistant Avatar, so as to accurately imitate the driver's clothing.”);
…… and animate the presentation digital avatar on a display device of the vehicle (Deng paragraph [0018] “the figure image generation module 102 may be implemented by a conventional animation generation tool or digital human production tool”, paragraph [0021] “the figure image generation module 102 generates the digital assistant Avatar on the basis of the figure of the passenger, so that the figure of the assistant presented on a screen matches the passenger.”).
Deng is not relied on for the below claim language ……extract a feature of an object in an external environment of the vehicle from a vehicle-captured image of the object; render a presentation digital avatar in a likeness of the occupant by transferring the feature of the object onto the base digital avatar of the occupant;……
Toshio teaches ……extract a feature of an object in an external environment of the vehicle from a vehicle-captured image of the object (Toshio paragraph [0073-0082] “some of them including analysis of images/videos obtained by cameras available in the vehicle …… computer vision techniques using information obtained from cameras installed in the vehicle are employed……The proposed computer vision module 60 is able to identify/detect: a plurality of pedestrian poses (e.g., standing up, arms up, pointing to the car, etc.); a plurality of human actions (e.g., walking, standing up, running, biking, using phone, etc.); a plurality of gestures (e.g., hand waving, head nod, thumbs up, “okay” gesture, left/right turn signal with arms, stop gesture with hand, etc.); gaze (e.g., looking to the car, looking to smartphone, distracted or looking to somewhere else); face expressions (e.g., happy, worried, sad, neutral, etc.); gender recognition (male, woman); age estimation (e.g.: kid, elder, etc.); and fashion/clothes recognition.”);
render a presentation digital avatar in a likeness of the occupant by transferring the feature of the object onto the base digital avatar of the occupant;…… (Toshoi teaches updating digital avatar based on extracted feature from pedestrian, Deng teaches updating digital avatar based on the features of vehicle environment, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Toshoi with Deng. Toshoi paragraph [0092] “Avatar generator module 70: Based on the inputs from Computer vision module 60 and Personalization module 80, …… this avatar generator module 70 generates (or updates) a digital avatar 10 to display. This avatar generation can be implemented using computer graphics, facial mapping/scanning/rendering, machine learning”, paragraph [0091] “In case of recognizing a kid, the avatar 20 could change appearance to a cartoon character and present more informal/relaxed messages. The same personalization could be done regarding the pedestrian gender”, Deng paragraph [0026-0027] “ the vehicle being in navigation may be driving through an urban road, or a corresponding sea or grassland color is presented when the vehicle passes by the seaside or grassland; and a navigation picture also changes with daytime, evening or dusk as time goes by. In addition, the vehicle environment may also be a sound environment …… FIG. 2 shows a configuration of a control device for changing the figure of a digital assistant Avatar on the basis of a vehicle environment”).
Deng and Toshoi are in the same field of endeavor, namely vehicle based avatar generation. Toshoi teaches extracting pedestrian features from images in the external environment of vehicle to improve user interaction (Toshoi paragraph [0083] “All these elements may contribute to increase the quality of human-like interactions of the self-driving car (i.e., the avatar) with the pedestrian/cyclist/driver, in different traffic situations and conditions.”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Toshoi with the method of Deng to improve user interaction.
Regarding claim 6, Deng in view of Toshoi teach The system of claim 1, and further teach wherein: the machine-readable instructions further comprise machine-readable instructions that, when executed by the processor, cause the processor to deploy a neural network to: identify defining visual characteristics of the object (Toshoi paragraph [0073] “Computer vision module 60: For understanding the environment around the vehicle many existing approaches for autonomous vehicles rely on Artificial Intelligence and machine learning techniques, some of them including analysis of images/videos obtained by cameras available in the vehicle.”, paragraph [0074-0082] “The proposed computer vision module 60 is able to identify/detect: a plurality of pedestrian poses (e.g., standing up, arms up, pointing to the car, etc.); a plurality of human actions (e.g., walking, standing up, running, biking, using phone, etc.); a plurality of gestures (e.g., hand waving, head nod, thumbs up, “okay” gesture, left/right turn signal with arms, stop gesture with hand, etc.); gaze (e.g., looking to the car, looking to smartphone, distracted or looking to somewhere else); face expressions (e.g., happy, worried, sad, neutral, etc.); gender recognition (male, woman); age estimation (e.g.: kid, elder, etc.); and fashion/clothes recognition.”);
and construct a theme for the presentation digital avatar based on the defining visual characteristics of the object (Deng teaches deciding feature elements from navigation interface image, Toshoi teaches extracting pedestrian features such as age, gender and fashion based on captured images as the navigation images, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Toshoi with the method of Deng. Deng paragraph [0028] “it is assumed that the current vehicle is in a navigation mode currently, the captured image features may be a navigation interface image GUImg, and thus, a plurality of feature elements F are determined on the basis of the captured image GUImg and include, but are not limited to, a time feature F1, such as evening or a specific moment, a road feature F2, such as a current urban road or rural road or highway, a color feature F.sub.3 of a current picture, a driving destination F.sub.4, etc.”);
and the machine-readable instruction that causes the processor to render the presentation digital avatar in the likeness of the occupant comprises a machine-readable instruction that causes the processor to deploy the neural network to transfer visual characteristics defined by the theme onto the base digital avatar of the occupant (Deng teaches using an AI model to update digital avatar based on the extracted feature elements, paragraph [0036-0037] “The figure image generation module 102 loads the clothing file ClothFile generated by the file generation module 103, thereby changing the figure of the digital assistant Avatar, for example, changing the clothing of the digital assistant Avatar from the style such as suits at work to relaxed casual clothing off work……the figure changing of the digital assistant Avatar is generated in real time, without relying on any database or any existing clothing option, but is determined in real time on the spot on the basis of the current environment by automatically driving the generative artificial intelligence models CDLM, CRGM, etc.”).
Deng and Toshoi are in the same field of endeavor, namely vehicle based avatar generation. Toshoi teaches extracting pedestrian features from images in the external environment of vehicle to improve user interaction (Toshoi paragraph [0083] “All these elements may contribute to increase the quality of human-like interactions of the self-driving car (i.e., the avatar) with the pedestrian/cyclist/driver, in different traffic situations and conditions.”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Toshoi with the method of Deng to improve user interaction.
Regarding claim 9, it recites similar limitations of claim 1 but in a non-transitory machine-readable medium form. The rationale of claim 1 rejection is applied to reject claim 9. In addition, Deng teaches A non-transitory machine-readable medium comprising instructions that, when executed by a processor, cause the processor to (Deng paragraph [0058] “The machine-readable instruction, when executed by a processor, causes the processor to execute any of the methods disclosed above in the description.”).
Regarding claim 13, claim 13 has similar limitations as claim 6, therefore it is rejected under the same rationale as claim 6.
Regarding claim 15, it recites similar limitations of claim 1 but in a method form. The rationale of claim 1 rejection is applied to reject claim 15. In addition, Deng teaches A method, comprising (Deng paragraph [0058] “the control apparatus for changing the figure of the digital assistant Avatar according to the present disclosure may be implemented as a processor or a main controller and a memory, wherein the memory stores various modules in a form of a computer program, and the processor may implement the method of the present disclosure by executing these modules.”).
Regarding claim 19, claim 19 has similar limitations as claim 6, therefore it is rejected under the same rationale as claim 6.
Claim(s) 2, 10 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US 20250316002 A1), hereinafter as Deng, in view of Toshio et al. (US 20200223352 A1), hereinafter as Toshio, further in view of Rao et al. (US 20200114931 A1), hereinafter as Rao.
Regarding claim 2, Deng in view of Toshio teach The system of claim 1, wherein the machine-readable instructions further comprise machine-readable instructions that, when executed by the processor, cause the processor to: but are not relied on for the below claim language detect an action of the occupant that identifies the object and a location of the object; and retrieve images captured by a vehicle camera with a field of view that overlaps the location of the object.
Rao teaches detect an action of the occupant that identifies the object and a location of the object; and retrieve images captured by a vehicle camera with a field of view that overlaps the location of the object (Rao Figure 3 and paragraph [0045] “An example camera view (e.g., based on the field of view 306) is shown at 308. A driver 310 may interact with and control the camera via gestures. For example, the driver 310 may point at an object of interest 312 in order to request that the camera focuses on or otherwise processes the object. Responsive to detecting the driver's pointing gesture (e.g., via one or more cabin-facing cameras configured to image the driver, passengers, and/or other objects in the cabin of the vehicle 304), the in-vehicle computing system 300 may send control instructions to the camera 302 and/or otherwise process images from the camera 302 in order to select a camera view based on the driver's pointing.”).
Deng, Toshoi and Rao are in the same field of endeavor, namely vehicle based user interaction. Rao teaches a method to allow driver specifying areas of interest to improve user interaction (Rao paragraph [0017] “The technologies may create a natural interaction with the vehicle occupants, anticipate user needs, and simplify task completion.”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Rao with the method of Deng and Toshoi to improve user interaction.
Regarding claim 10, claim 10 has similar limitations as claim 2, therefore it is rejected under the same rationale as claim 2.
Regarding claim 16, claim 16 has similar limitations as claim 2, therefore it is rejected under the same rationale as claim 2.
Claim(s) 3, 11 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US 20250316002 A1), hereinafter as Deng, in view of Toshio et al. (US 20200223352 A1), hereinafter as Toshio, further in view of Niemz et al. (DE 102014207897 A1), hereinafter as Niemz. The original and a machine translation of Niemz are provided by the examiner.
Regarding claim 3, Deng in view of Toshio teach The system of claim 1, wherein the machine-readable instructions further comprise machine-readable instructions that, when executed by the processor, cause the processor to: but are not relied on for the below claim language determine a level of detail of the feature of the object in the vehicle-captured image; responsive to the level of detail being below a threshold amount: identify, from a log of vehicle-captured images, a previously captured image of the object; and extract the feature of the object from the previously captured image of the object.
Niemz teaches determine a level of detail of the feature of the object in the vehicle-captured image (Niemz teaches an image parameter of sharpness or blurredness as a level of detail of feature, paragraph [0008] “it is preferably intended that the image parameter is the sharpness of the recorded image.”); responsive to the level of detail being below a threshold amount: (Niemz paragraph [0005] “whereby the image parameter in the first image area falls below a predefined threshold”): identify, from a log of vehicle-captured images, a previously captured image of the object (Niemz teaches using a stored image section for blurry areas, paragraph [0005] “it is found that at least a second image area is present in stored images, wherein the second image area shows the same area of the environment as the first image area, and wherein the image parameter in the second image area does not fall below the threshold. Analogous to the above procedure, the first image area can therefore be corrected with respect to the parameter by the second image area.”); and extract the feature of the object from the previously captured image of the object (Niemz teaches replace blurred image area with stored sharp area, Toshoi teaches extracting pedestrian feature from captured image, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Niemz with Toshoi. Niemz paragraph [0008] “sharp image areas must be separated from blurred image areas, whereby the blurred image areas are replaced by stored image areas. Therefore, blurry areas are preferably replaced by sharp areas from previously taken images. “).
Deng, Toshoi and Niemz are in the same field of endeavor, namely vehicle based image processing. Niemz teaches a method to improve the quality of captured images (Niemz paragraph [0027] “Due to the movement described above, it is therefore possible that the second image areas replace the first image areas 7. This results in a display image 5 whose quality is significantly better than without such a replacement.”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Niemz with the method of Deng and Toshoi to improve image quality.
Regarding claim 11, claim 11 has similar limitations as claim 3, therefore it is rejected under the same rationale as claim 3.
Regarding claim 17, claim 17 has similar limitations as claim 3, therefore it is rejected under the same rationale as claim 3.
Claim(s) 4-5, 12 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US 20250316002 A1), hereinafter as Deng, in view of Toshio et al. (US 20200223352 A1), hereinafter as Toshio, further in view of Niemz et al. (DE 102014207897 A1), hereinafter as Niemz, and Slutsky et al. (US 20230150429 A1), hereinafter as Slutsky. The original and a machine translation of Niemz are provided by the examiner.
Regarding claim 4, Deng in view of Toshoi teach The system of claim 1, wherein the machine-readable instructions further comprise a machine-readable instruction that, when executed by the processor, and further teach causes the processor to deploy a neural network trained to: receive the vehicle-captured image of the object from a vehicle camera (Toshoi paragraph [0073] “Computer vision module 60: For understanding the environment around the vehicle many existing approaches for autonomous vehicles rely on Artificial Intelligence and machine learning techniques, some of them including analysis of images/videos obtained by cameras available in the vehicle.”); extract, from the vehicle-captured image, a distinguishing feature of the object (Toshoi paragraph [0074-0082] “The proposed computer vision module 60 is able to identify/detect: a plurality of pedestrian poses (e.g., standing up, arms up, pointing to the car, etc.); a plurality of human actions (e.g., walking, standing up, running, biking, using phone, etc.); a plurality of gestures (e.g., hand waving, head nod, thumbs up, “okay” gesture, left/right turn signal with arms, stop gesture with hand, etc.); gaze (e.g., looking to the car, looking to smartphone, distracted or looking to somewhere else); face expressions (e.g., happy, worried, sad, neutral, etc.); gender recognition (male, woman); age estimation (e.g.: kid, elder, etc.); and fashion/clothes recognition.”); ……
Deng and Toshoi are in the same field of endeavor, namely vehicle based avatar generation. Toshoi teaches extracting pedestrian features from images in the external environment of vehicle to improve user interaction (Toshoi paragraph [0083] “All these elements may contribute to increase the quality of human-like interactions of the self-driving car (i.e., the avatar) with the pedestrian/cyclist/driver, in different traffic situations and conditions.”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Toshoi with the method of Deng to improve user interaction.
Deng in view of Toshoi are not relied on for the below claim language …… identify the object in other images of a corpus of digital content based on the distinguishing feature; and render a digital model of the object by combining the vehicle-captured image of the object with the other images of the object.
Niemz teaches …… identify the object in other images of a corpus of digital content based on the distinguishing feature; and render …… by combining the vehicle-captured image of the object with the other images of the object (Niemz teaches combining image with previously stored image based on the quality of image parameter, paragraph [0005] “The next step is to determine that at least one image area is present in the recorded image, whereby the image parameter in the first image area falls below a predefined threshold. Finally, it is found that at least a second image area is present in stored images, wherein the second image area shows the same area of the environment as the first image area, and wherein the image parameter in the second image area does not fall below the threshold. Analogous to the above procedure, the first image area can therefore be corrected with respect to the parameter by the second image area. Thus, a display image is generated, whereby the display image is created from the multitude of recorded images”).
Deng, Toshoi and Niemz are in the same field of endeavor, namely vehicle based image processing. Niemz teaches a method to improve the quality of captured images (Niemz paragraph [0027] “Due to the movement described above, it is therefore possible that the second image areas replace the first image areas 7. This results in a display image 5 whose quality is significantly better than without such a replacement.”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Niemz with the method of Deng and Toshoi to improve image quality.
Deng in view of Toshoi and Niema are not relied on for the below claim language a digital model of the object. Slutsky teaches a digital model of the object (Slutsky paragraph [0006] “Using an AI Neural Network (NN), object recognition analyzes the 2D camera-generated image to classify characteristics from the image and associate the characteristics of a target object with a corresponding model collection set. The target object is removed and replaced with a 3D object model universal to that set, e.g., in a new “virtual” image.”).
Deng, Toshoi, Niemz and Slutsky are in the same field of endeavor, namely vehicle based image processing. Slutsky teaches using a 3D digital model to represent camera captured image to improve user interaction (Slutsky paragraph [0007] “Attendant benefits for at least some of the disclosed concepts include target acquisition systems that more accurately detect and classify target objects within two-dimensional (2D) camera images, e.g., to provide more comprehensive situational awareness and, thus, enable more intuitive driver and vehicle responses.”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Slutsky with the method of Deng, Toshoi and Niemz to improve user interaction.
Regarding claim 5, Deng in view of Toshoi, Niemz and Slutsky teach The system of claim 4, and further teach wherein the machine-readable instruction that causes the processor to deploy the neural network trained comprises a machine-readable instruction that causes the processor to infer features of the object omitted from the vehicle-captured image of the object and the other images of the object (Slutsky paragraph [0006] “Such object classification and replacement may be particularly useful for scenarios in which parts of an object are obfuscated from or not visible to the resident camera system and, thus, object rendering from a desired viewpoint is partial.”).
Deng, Toshoi, Niemz and Slutsky are in the same field of endeavor, namely vehicle based image processing. Slutsky teaches using a 3D digital model to represent camera captured image to improve user interaction (Slutsky paragraph [0007] “Attendant benefits for at least some of the disclosed concepts include target acquisition systems that more accurately detect and classify target objects within two-dimensional (2D) camera images, e.g., to provide more comprehensive situational awareness and, thus, enable more intuitive driver and vehicle responses.”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Slutsky with the method of Deng, Toshoi and Niemz to improve user interaction.
Regarding claim 12, claim 12 has similar limitations as claim 4, therefore it is rejected under the same rationale as claim 4.
Regarding claim 18, claim 18 has similar limitations as claim 4, therefore it is rejected under the same rationale as claim 4.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Deng et al. (US 20250316002 A1), hereinafter as Deng, in view of Toshio et al. (US 20200223352 A1), hereinafter as Toshio, further in view of Zhang et al. (CN 115366834 A), hereinafter as Zhang. The original and a machine translation of Zhang are provided by the examiner. The paragraph of the machine translation of Zhang starts with both number and letter ‘n’.
Regarding claim 8, Deng in view of Toshio teach The system of claim 6, wherein the machine-readable instruction that causes the processor to deploy the neural network, comprises a machine-readable instruction that causes the processor to deploy the neural network to: but are not relied on for the below claim language construct an auditory theme for the presentation digital avatar based on the defining visual characteristics of the object; and output audio associated with the presentation digital avatar based on the auditory theme.
Zhang teaches construct an auditory theme for the presentation digital avatar based on the defining visual characteristics of the object (Zhang teaches a neural network model to decide the scene type of the exterior environment of the vehicle, the costume of the displayed 3D image and the music played are all adjusted based on the scene type, paragraph [n0084] “Specifically, for example, environmental information from various scenarios such as desert, grassland, snowfall, rainfall, and Spring Festival can be collected to form a sample set for training a basic neural network model, thereby obtaining a classification model.”, paragraph [n0057] “Adjust the interior environment of the vehicle according to the scene type to match the exterior environment of the vehicle.”);and output audio associated with the presentation digital avatar based on the auditory theme (Zhang paragraph [n0062] “When the scene outside the car is detected to be the Spring Festival …… the 3D image on the center console screen is dressed in Tang suit …… the multimedia device automatically pushes Spring Festival related videos or music”)
Deng, Toshoi and Zhang are in the same field of endeavor, namely vehicle based avatar generation. Zhang teaches adjusting the interior of vehicle based on the scene type of the outside of the vehicle to improve user interaction (Zhang paragraph [n0002] “The interaction between cars and people is increasing,and improving the user's driving experience has become an important direction for the development of the automotive industry.”). Therefore, it would have been obvious for a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Zhang with the method of Deng and Toshoi to improve user interaction.
Allowable Subject Matter
Claims 7, 14 and 20 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.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claims 7, 14 and 20, the closest prior art of Watanabe et al. (US 20160005171 A1), hereinafter as Watanabe, teaches a method of searching similar image based on feature detected in input image to decide the type of scene (Watanabe paragraph [0033] “The image analysis device 105 is a device that detects an object region in an input image, and decides a parameter at the time of detection through scene determination using a similar image search……A scene is a term describing a scene shown in an image, and refers to, for example, “indoor,” “landscape,” “downtown,” and the like……the scene determination refers to searching a database for an image having an image feature quantity similar to an image feature quantity of an input image”). However, Watanabe fails to teach the combined limitation below as a whole “comprises a machine-readable instruction that causes the processor to deploy the neural network to: identify other images in a corpus of digital content that have same or similar defining visual characteristics as the object; and aggregate defining visual characteristics of the object and the other images to construct the theme”. Furthermore, no prior art of record either alone or in combination teaches the above limitation as a whole. Therefore, claims 7, 14 and 20 are considered to be allowable.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bandai et al. (US 20210150195 A1) teaches a method of extracting a person’s clothing information based on images captured by an in-vehicle camera.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIAOMING WEI whose telephone number is (571)272-3831. The examiner can normally be reached M-F 8:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kee Tung can be reached at (571)272-7794. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/XIAOMING WEI/Examiner, Art Unit 2611
/KEE M TUNG/Supervisory Patent Examiner, Art Unit 2611
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