DETAILED ACTION
This action is in response to the application filed on November 27th, 2024. Claims 1-20 are pending and have been examined.
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 § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 10 and 20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claims contain subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. Claim 1 recites “obtain coordinates of an object from an image comprising the object” and claim 10 recites “apply[ing] a second weight to the coordinates”. The specification, while describing how to align coordinate systems and use the coordinates of an object to perform depth map creation using a neural network, does not enable a person of ordinary skill in the art to make and use the invention as claimed without undue experimentation. Specifically, the object coordinates which were defined in claim 1 are weighted in claim 10, but it is not described how this weighting would be performed and therefore defines an invention that the specification fails to enable. The specification only mentions the weighting of the coordinates with the same language as the claims and does not provide further details. No portion of the specification or figures enables a weighted coordinate
The wands factors are as follows:
Breadth of the claims: the claims cover all possible implementations of weighting a coordinate. The specification enables none of these possible implementations.
The level of predictability in the art: neural network are used for a range of applications, and a small change to the inputs or inner workings of a neural network can have large downstream effects on the outputs, resulting in unpredictability.
Amount of direction provided by the inventor: the specification provides zero guidance on how to apply a weight to a coordinate, e.g., there is no disclosure of why a coordinate would be given a weight.
Absence of working examples: the specification contains no working examples of weighted coordinates and their applications.
Quantity of experimentation needed: to practice the claimed invention, a person having ordinary skill in the art would have to independently solve non-trivial problems including developing a weighted coordinate that does not change the portion of the image which the coordinate refers to.
In conclusion, the aforementioned wands factors weigh towards a finding of undue experimentation. The claimed scope of weighting a coordinate is not enabled by the specification and figures.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. As discussed with respect to the 112(a) written description rejection for the claim limitation “apply a second weight to the coordinates”, it is unclear how a coordinate can be weighted without fundamentally changing what the coordinate refers to. For example, a geometric transformation such as a dilation is done by multiplying the coordinates of a shape by a value. Would this be considered applying a weight to a coordinate? The metes and bounds of this limitation are not clear, and the specification does not provide further detail as to what would be considered applying a weight to a coordinate.
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 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, 9, 11, 13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US20210174528 (herein after referred to by its primary author, Mordechai) in view of US20230281873 (herein after referred to by its primary author, Engstle).
In regards to claim 1, Mordechai teaches an apparatus for controlling autonomous driving of a vehicle, the apparatus comprising: a first sensor; a second sensor; a memory configured to store a neural network model; and a processor (Mordechai Paragraph [0041] “The system 300 may include a processor 340, a polarimetric camera 320, a lidar 322, a memory 345, a vehicle controller 330 a throttle controller 355, a brake controller 360 and a steering controller 370.”) configured to: obtain a first depth map by inputting at least one of the image or the coordinates into the neural network model (Mordechai Paragraph [0036] “The presently disclosed system and methodology is configured to generate real-time video of dense 3D point clouds using a polarization camera without using an active illumination. The 3D point cloud may be generated using a deep neural network in real-time. The neural network may receive high-resolution polarization + RGB frames and may output a ‘RGB + depth point cloud.”); obtain, based on a cluster of points acquired by the second sensor, a second depth map; and determine, based on comparing the first depth map and the second depth map, a difference between the first depth map and the second depth map; output a signal indicating the difference (Mordechai Paragraph [0016] “In accordance with another aspect of the present invention including receiving a lidar depth cloud of the field of view from a lidar and comparing the depth map to the lidar depth cloud to reaffirm the depth map.”; Paragraph [0036] “In an additional embodiment, a neural network may be trained in a fully automated process using a ground truth training apparatus comprised of the polarimetric imaging camera aligned with high resolution depth imager such as a dense LiDAR, stereoscopic camera, structured light depth imager, or any other adequate sensor.”); and control, based on the signal, autonomous driving of the vehicle (Mordechai Paragraph [0005] “Disclosed herein are autonomous vehicle control system training systems and related control logic for provisioning autonomous vehicle control, methods for making and methods for operating such systems, and motor vehicles equipped with onboard control systems.”).
Mordechai does not teach obtaining coordinates of an object from an image comprising the object wherein the image is acquired by the first sensor based on at least one of an intrinsic parameter of the first sensor, an extrinsic parameter of the first sensor, or a distortion coefficient of the first sensor.
However, Engstle teaches obtaining coordinates of an object from an image comprising the object (Engstle Paragraph [0095] “The external camera system 14 determines the position of the calibration objects 9 in relation to the vehicle coordinate system 8.”) wherein the image is acquired by the first sensor based on at least one of an intrinsic parameter of the first sensor, an extrinsic parameter of the first sensor, or a distortion coefficient of the first sensor (Engstle Paragraph [0034] “The multi-stage calibration process described below is realised in a calibration hall via a diversion of the extrinsic reference coordinate system to vehicle coordinate system calibration, which is why the reference sensor system for this calibration step is mounted on a vehicle”).
Engstle is considered to be analogous to the claimed invention because they are both in the same field of calibration of cameras in autonomous driving vehicles. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Mordechai to include the teachings of Engstle, to provide the advantage of calibrating the sensor to work with any make or model of vehicle (Engstle Paragraph [0017] “After carrying out the method for calibrating the portable reference sensor system according to the invention, the reference sensor system can be transported and can now be used on any vehicle independently of the vehicle used during the method, without recalibration to the vehicle now in use. This makes it possible that only the portable reference sensor system has to be brought to the respective place of use without having to rely on a calibration hall or an external calibration.”)
In regards to claim 3, Mordechai in view of Engstle teaches the apparatus of claim 1, wherein the processor is configured to, based on an angle between a first reference line facing a front of the vehicle and a second reference line formed with respect to an optical axis of the first sensor exceeding a first reference angle, perform automatic online calibration to realign the second reference line with respect to the first reference line, wherein the vehicle comprises the first sensor (Engstle Figure 1; Paragraph [0073] “An angular offset 13 of, for example, only 1° between the vehicle coordinate system 8 and the reference coordinate system 7 of the reference sensor system 1, leads trigonometrically to a lateral positioning deviation of the detected object of approx. 1.75 metres at a distance of 100 metres, as can be seen in FIG. 1.” Examiner note: Figure 1 shows that if the optical axis deviates from the front forwards reference line, that calibration need to be performed to align the vehicle coordinate system and the reference coordinate system.).
In regards to claim 9, Mordechai in view of Engstle teaches the apparatus of claim 1, wherein the processor is configured to train, based on the first depth map and the second depth map, the neural network model to reduce a size of the difference (Mordechai Paragraph [0036] “In an additional embodiment, a neural network may be trained in a fully automated process using a ground truth training apparatus comprised of the polarimetric imaging camera aligned with high resolution depth imager such as a dense LiDAR, stereoscopic camera, structured light depth imager, or any other adequate sensor.”; Paragraph [0048] “The network output includes 1 channel representing Depth. The problem may be posed as a ‘Regression’ problem where the ‘Loss’ (i.e. the difference between Predicted Depth and True Depth) may be defined in many ways such as L1 Loss, Huber Loss (Smooth L1 Loss), and L2 Loss.”).
In regards to claim 11, Mordechai in view of Engstle renders obvious the claim limitations as in the consideration of claim 1.
In regards to claim 13, Mordechai in view of Engstle renders obvious the claim limitations as in the consideration of claim 3.
In regards to claim 19, Mordechai in view of Engstle renders obvious the claim limitations as in the consideration of claim 9.
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Mordechai in view of Engstle as applied to the claims above, and further in view of “What Is Camera Calibration?” (herein after referred to as MATLAB).
In regards to claim 2, Mordechai in view of Engstle teaches the apparatus of claim 1, wherein the processor is configured to: determine, based on a position of the first sensor on the vehicle, the extrinsic parameter (Engstle Paragraph [0083] “a) Calibration of the optical sensors 3 of the reference sensor system 1 to a predetermined reference coordinate system 7 by determining a rotation matrix and/or translation matrix of each sensor 3, 6, so that a coordinate system of each sensor is calibrated to the reference coordinate system 7, the respective rotation matrices and/or translation matrices being determined by detecting external calibration objects 9”).
Mordechai in view of Engstle does not teach determining, based on feature modeling for the first sensor, the intrinsic parameter and the distortion coefficient.
However, MATLAB teaches determining, based on feature modeling for the first sensor, the intrinsic parameter and the distortion coefficient (MATLAB Pinhole Camera Model and Distortion in Camera Calibration Examiner note: The figures in these section show models of a camera in a 3D scene and a model of distortion from different distortion types).
Mordechai in view of Engstle teaches that calibration of the optical sensors to remove distortion should be performed before the steps of their disclosure (Engstle Paragraph [0026] “According to a further embodiment, it is provided that prior to process step a) the optical sensors, which are given by cameras, are calibrated in such a way that distortion effects of the respective lenses or camera lenses are eliminated.”). MATLAB teaches a method of performing optical correction to remove distortion. One of ordinary skill in the art could appreciate that the elements of MATLAB could be substituted into the distortion correction of Mordechai in view of Engstle, and each method would still perform the same function as they do separately, Mordechai in view of Engstle performing calibration of the sensors, and MATLAB performing distortion correction. The results of this combination would be predictable, since the elements still perform their own functions, and Engstle suggests that images should be corrected for distortion prior to their methods. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Mordechai in view of Engstle to include the teachings of MATLAB.
In regards to claim 12, Mordechai in view of Engstle and MATLAB renders obvious the claim limitations as in the consideration of claim 2.
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Mordechai in view of Engstle as applied to the claims above, and further in view of US20220334238 (herein after referred to as Stachnik).
In regards to claim 4, Mordechai in view of Engstle teaches the apparatus of claim 1, wherein the processor is configured to obtain a second vehicle coordinate system based on rotating a first vehicle coordinate system by a second reference angle, and wherein the vehicle comprises the first sensor; obtain, based on shifting the second vehicle coordinate system by a reference distance, a third vehicle coordinate system corresponding to a first sensor coordinate system, wherein the first sensor coordinate system is formed with respect to the first sensor; and obtain the coordinates, wherein the third vehicle coordinate system comprises the extrinsic parameter (Engstle Paragraph [0083] “a) Calibration of the optical sensors 3 of the reference sensor system 1 to a predetermined reference coordinate system 7 by determining a rotation matrix and/or translation matrix of each sensor 3, 6, so that a coordinate system of each sensor is calibrated to the reference coordinate system 7, the respective rotation matrices and/or translation matrices being determined by detecting external calibration objects 9”).
Mordechai in view of Engstle does not teach wherein the first vehicle coordinate system is formed with respect to a center point of a front bumper of the vehicle.
However, Stachnik teaches wherein the first vehicle coordinate system is formed with respect to a center point of a front bumper of the vehicle (Stachnik Figure 1; Paragraph [0042] “FIG. 1 also depicts a vehicle coordinate system 17 having an origin which is located at a center of a front bumper of the vehicle 10.”).
Mordechai in view of Engstle teaches that the zero point of the sensor coordinate system should be displaced/translated to the zero point of the vehicle coordinate system (Engstle Paragraph [0015] “Usually, the three axes of the reference sensor system are directed in a forward direction, in an elevation direction and in a lateral direction of the reference sensor system. In order to determine the position of the reference sensor system relative to the vehicle, it is particularly necessary to determine a displacement or translation (for example by means of a vector) of the zero point of the coordinate system of the reference sensor system relative to the zero point of the coordinate system of the vehicle, in particular to the nearest millimetre.”). However, Mordechai in view of Engstle does not teach that the zero point should be located at the front bumper of the car. Stachnik teaches that the zero point of a vehicle system should be located at the front bumper of the car. One of ordinary skill in the art could appreciate that this element of Stachnik could be substituted into the methods of Mordechai in view of Engstle, and the combination would be predictable and result in a system that aligns all sensors to a single reference system that is centered at the front bumper of the car. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Mordechai in view of Engstle to include the teachings of Stachnik.
In regards to claim 14, Mordechai in view of Engstle and Stachnik renders obvious the claim limitations as in the consideration of claim 4.
Claims 5-6 and 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Mordechai in view of Engstle and Stachnik as applied to the claims above, and further in view of MATLAB.
In regards to claim 5, Mordechai in view of Engstle and Stachnik teaches the apparatus of claim 4, wherein the processor is configured to applying a specified equation to the third vehicle coordinate system, wherein the specified equation comprises the distortion and obtaining, based on the distortion, the coordinates (Engstle Paragraph [0026] “According to a further embodiment, it is provided that prior to process step a) the optical sensors, which are given by cameras, are calibrated in such a way that distortion effects of the respective lenses or camera lenses are eliminated.” Examiner note: Engstle teaches that distortion correction could be applied before the camera image is processed further, such as for creating the third vehicle coordinate system and obtaining the object’s coordiantes)
Mordechai in view of Engstle and Stachnik does not teach obtaining a first matrix by applying a specified equation to the third vehicle coordinate system, wherein the specified equation comprises the distortion coefficient.
However, MATLAB teaches obtaining a first matrix by applying a specified equation to the third vehicle coordinate system, wherein the specified equation comprises the distortion coefficient (MATLAB “Distortion in Camera Calibration” Examiner note: This section teaches that distortion of an image caused by the lens can be corrected using an equation which includes distortion coefficients).
Mordechai in view of Engstle and Stachnik teaches that calibration of the optical sensors to remove distortion should be performed before the steps of their disclosure (Engstle Paragraph [0026] “According to a further embodiment, it is provided that prior to process step a) the optical sensors, which are given by cameras, are calibrated in such a way that distortion effects of the respective lenses or camera lenses are eliminated.”). MATLAB teaches a method of performing optical correction to remove distortion. One of ordinary skill in the art could appreciate that the elements of MATLAB could be substituted into the distortion correction of Mordechai in view of Engstle and Stachnik, and each method would still perform the same function as they do separately, Mordechai in view of Engstle and Stachnik performing calibration of the sensors, and MATLAB performing distortion correction. The results of this combination would be predictable, since the elements still perform their own functions, and Engstle suggests that images should be corrected for distortion prior to their methods. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Mordechai in view of Engstle and Stachnik to include the teachings of MATLAB.
In regards to claim 6, Mordechai in view of Engstle, Stachnik, and MATLAB teaches the apparatus of claim 5, wherein the processor is configured to: obtain, based on at least one of a focal length of the first sensor, a skew coefficient of the first sensor, or a principal point of the image, a second matrix (MATLAB Intrinsic Parameters “The intrinsic parameters include the focal length, the optical center, also known as the principal point, and the skew coefficient. The camera intrinsic matrix, K”); obtain, based on the first matrix and the second matrix, the coordinates (Engstle Paragraph [0027] “Particularly preferably, the specific focal length of the respective camera lens is determined by means of a calibration routine of the reference sensor system, particularly preferably taking into account the manufacturing tolerances. The specific focal length is taken into account when generating a camera image.”); and obtain the first depth map by inputting the coordinates into the neural network model (Mordechai Paragraph [0048] “The network input may include three RGB channels and up to four channels of polarization data. Stacking additional channels with the 3 RGB channels is a standard procedure and requires modification of the 1st layer only.” Examiner note: When considering Mordechai in view of Engstle, the calibration of Engstle would be performed before the depth estimation of Mordechai. Furthermore, Engstle uses the coordinates of the object to calibrate the sensor and create the rotation and translation matrices. Therefore, the coordinates of the object would be used when creating the first depth map of Mordechai in view of Engstle, as the depth estimation would be performed using the calibrated camera with the rotated and translated corrected image).
In regards to claim 15, Mordechai in view of Engstle, Stachnik, and MATLAB renders obvious the claim limitations as in the consideration of claim 5.
In regards to claim 16, Mordechai in view of Engstle, Stachnik, and MATLAB renders obvious the claim limitations as in the consideration of claim 6.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Mordechai in view of Engstle as applied to the claims above, and further in view of US20150098623 (herein after referred to as Shimzu).
In regards to claim 7, Mordechai in view of Engstle teaches the apparatus of claim 1, wherein the processor is configured to obtain the coordinates, wherein the vehicle comprises the first sensor (Engstle Paragraph [0095] “The external camera system 14 determines the position of the calibration objects 9 in relation to the vehicle coordinate system 8.”).
However, Mordechai in view of Engstle fails to teach obtaining, based on a plurality of planes separated with respect to a reference axis of the vehicle, the coordinates; and wherein the reference axis comprises an axis perpendicular to a ground with respect to a specified position of the vehicle.
However, Shimzu teaches obtaining, based on a plurality of planes separated with respect to a reference axis of the vehicle, the coordinates; and wherein the reference axis comprises an axis perpendicular to a ground with respect to a specified position of the vehicle (Shimzu Figure 14 ZCAR; Abstract “An image processing apparatus that, based on an image imaged by a camera installed on a car and distance to a measurement point on a peripheral object computed by a range sensor installed on the car, draws virtual three-dimensional space in which a surrounding environment around the car is reconstructed.”).
Mordechai in view of Engstle teaches aligning sensors to a single coordinate system. However, Mordechai in view of Engstle does not teach that the coordinate system is made up of a plurality of planes. Shimzu teaches that a coordinate system of a car could be made up a of a plurality of planes parallel to the ground in the z-direction. One of ordinary skill in the art could appreciate that this element of Shimzu could be substituted into the methods of Mordechai in view of Engstle, and the combination would be predictable and result in a system that aligns all sensors to a single reference system that is composed of a plurality of planes representing the 3rd “z” dimension. These planes would merely act as a boundary for step increases in the z-axis. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Mordechai in view of Engstle to include the teachings of Shimzu.
In regards to claim 17, Mordechai in view of Engstle and Shimzu renders obvious the claim limitations as in the consideration of claim 7.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Mordechai in view of Engstle as applied to the claims above, and further in view of “Latent Distribution-Based 3D Hand Pose Estimation From Monocular RGB Images” (herein after referred to as Li).
In regards to claim 8, Mordechai in view of Engstle teaches the apparatus of claim 1, wherein the neural network model comprises an encoder into which the image is inputted and a decoder (Mordechai Paragraph [0048] “In one exemplary embodiment, the processor 340 may employ a convolutional encoder-decoder to transform the images and polarization information into the depth map.”), and wherein the neural network model is configured to: obtain image features for input to the decoder by inputting the image to the encoder (Mordechai Paragraph [0048] “The encoder receives the RGB and polarization image and generates a low dimension representation. The decoder reverses the encoder's operation and is also comprised of layers, each containing convolution, pooling, normalization, and a non-linear activation function.”); and output, based on the image features, the first depth map (Mordechai Paragraph [0048] “The network output includes 1 channel representing Depth”).
Mordechai in view of Engstle does not teach a decoder into which the coordinates are inputted.
However, Li teaches a decoder into which the coordinates are inputted (Li Figure 2 E-D Net Examiner note: The bottom E-D net takes as input the PM (2D probability map) from the first E-D net, which is stated to represent the estimated depth coordinates in section V D. Then, the coordinates are input into the decoder, and multiple outputs from the encoder are fed into the decoder, as represented by the red lines in E-D net. Therefore, the input to the bottom E-D net is 2D coordinates, and those coordinates are used by the encoder and passed to the decoder, which fits the BRI of the claim language.).
Li is considered to be analogous to the claimed invention because they are in the same field of estimating depth maps from 2D images. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the system of Mordechai in view of Engstle to include the teachings of Li, to provide the advantage of compressing 2D and depth information into a single feature map (Li Section III “The first branch leverages the 2D coordinates (in the image plane) of hand joints and serves as guidance during training. The final 3D coordinates are obtained via our proposed latent distribution representation (LDR) in the second branch. In this work, we propose the LDR for 3D hand pose that avoids the channel inconsistency between the 2D and the depth estimation in previous works (e.g., latent heatmap representation (LHR), which was proposed by Iqbal et al. [20]) by compressing the 2D and depth information of the 3D hand pose into a single feature map for each joint.”)
In regards to claim 18, Mordechai in view of Engstle and Li renders obvious the claim limitations as in the consideration of claim 8.
No art rejection has been applied to claims 10 and 20, however due to their 35 U.S.C. 112(a)/(b) rejections, they are not indicated as allowable subject matter.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“Research of Camera Calibration Based on Genetic Algorithm BP Neural Network” describes a method of calibrating a camera’s parameters using a neural network.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CALEB LOGAN ESQUINO whose telephone number is (703)756-1462. The examiner can normally be reached M-Fr 8:00AM-4:00PM EST.
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/CALEB L ESQUINO/ Examiner, Art Unit 2677
/ANDREW W BEE/ Supervisory Patent Examiner, Art Unit 2677
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