DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
2. Claims 17-20 are amended.
3. Claims 1-16 are as previously presented.
Claim Rejections - 35 USC § 103
4. 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.
5. 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.
6. Claims 1, 4, 8, 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kim (US 12322359 B2), hereinafter Kim, in view of Chou (US 20200043135 A1), hereinafter Chou.
Regarding claim 1, Kim teaches a display module comprising: receiving a frame for display on a display panel (Fig. 5, Col 14 lines 4-15, panel-based display, wherein the display receiving and displaying an image based on an image signal is interpreted as receiving an image frame for display); implementing a machine-learning-based algorithm to upscale the frame to generate an upscaled frame (Fig. 10, Col. 25 line 43 – Col. 26 line 3, wherein learning processor DRL is defined as a deep reinforcement learning processor and interpreted as a machine-learning-based algorithm, which is used to increase a resolution of an on screen display to a greater resolution to generate an output image, which is interpreted as upscaling a frame); and send the upscaled frame to the display panel (Fig. 10, Col. 25 lines 36-42, wherein synthesized upscaled image is outputted to display 180).
Kim does not teach the display module comprising a timing controller to receive a frame for display on a display panel.
Chou teaches a display module comprising a timing controller to receive a frame for display on a display panel (Fig. 3, paragraph 43, wherein the controller processor may be a timing controller within a display, which is interpreted as a display module comprising a timing controller; paragraph 45 wherein the controller processes image data implemented as frame buffer; paragraph 41 wherein the image data processing pipeline controlled by the controller processor can be a display pipeline, which is interpreted as the timing controller receiving a frame for display on a display panel).
It would be obvious before the effective filing date of the claimed invention to have modified Kim to incorporate the teachings of Chou for this display module for upscaling frames. Kim discloses an image display device configured to upscale content from a first resolution to a greater resolution, utilizing machine learning to adjust the luminance and transparency of the content as part of outputting the upscaled content. Similarly, Chou discusses an engine for converting a lower resolution input image to a higher resolution output image that involves using a neural network to upscale the luminance channel and color channels of the input data. As both references utilize neural networks and similar processes for upscaling input data, it would be obvious to combine them.
Regarding claim 4, Kim in view of Chou discloses the display module of claim 1. Additionally, Chou teaches the display module of claim 1, wherein the timing controller is to: receive a first segment of the frame (Fig. 3, paragraph 43, wherein controller 340 may include a timing controller; Fig. 6, paragraph 66-67, wherein controller 340 controls and coordinates components in a super resolution engine for upscaling images, which is interpreted as receiving an input image frame, including the first segment of the image frame); and implement the machine-learning-based algorithm to upscale the first segment of the frame while receiving a second segment of the frame (Fig. 6, paragraph 80, wherein neural network 612 upscales enhanced image data which is interpreted as a first segment of the frame; Fig. 6, paragraph 84-85, wherein feature detection processor 604 determines features from the image, which includes segmenting the image data, which is interpreted as receiving a second segment of the frame; Fig. 6 is interpreted as showing the neural network and feature detection processor working in parallel).
The motivation to combine would be the same as that set forth for claim 1.
Regarding claim 7, Kim teaches a compute device comprising: a display module; and display controller circuitry to: (Fig. 2, Col. 5 line 61 – Col. 6 line 11, wherein the image display apparatus 100 is interpreted as a display module; Fig. 2, Col. 7 line 65 – Col. 8 line 7, wherein signal processor 170 is interpreted as a display controller circuitry) implement a machine-learning-based algorithm to upscale the frame to generate an upscaled frame (Fig. 10, Col. 25 line 43 – Col. 26 line 3, wherein learning processor DRL is defined as a deep reinforcement learning processor and interpreted as a machine-learning-based algorithm, which is used to increase a resolution of an on screen display to a greater resolution to generate an output image, which is interpreted as upscaling a frame); and display the upscaled frame on a display panel of the display module (Fig. 10, Col. 25 lines 36-42, wherein synthesized upscaled image is outputted to display 180; Fig. 2, Col. 5 line 61 – Col. 6 line 11, wherein the display 180 is a display panel of the image display apparatus).
Kim does not teach a display module; and display controller circuitry to: generate a frame; and send the frame to the display module.
Chou teaches a display module; and display controller circuitry (Fig. 2, paragraph 34, display and display controller circuitry to send image data to the display) to: generate a frame; and send the frame to the display module (Fig. 3, paragraph 40-41, wherein the display controller may include an image data processing pipeline and can be a display pipeline, which is interpreted as sending image data to the display module; paragraph 45, wherein the image data may be processed and stored in a frame buffer, which is interpreted as generating a frame to send to the display module).
The motivation to combine would be the same as that set forth for claim 1.
Regarding claim 8, Kim discloses the compute device of claim 7. Additionally, Chou teaches the compute device of claim 7, wherein to implement, by the display module, the machine-learning-based algorithm to upscale the frame to generate the upscaled frame comprises to implement, by a timing controller of the display module (Fig. 3, paragraph 43, wherein the controller processor may be a timing controller within a display, which is interpreted as a display module comprising a timing controller), the machine-learning-based algorithm to upscale the frame to generate the upscaled frame (Fig. 10, Col. 25 line 43 – Col. 26 line 3, wherein learning processor DRL is defined as a deep reinforcement learning processor and interpreted as a machine-learning-based algorithm, which is used to increase a resolution of an on screen display to a greater resolution to generate an output image, which is interpreted as upscaling a frame).
Regarding claim 13, Kim discloses the compute device of claim 7. Additionally, Chou teaches the compute device of claim 7, wherein the timing controller is to: receive a first segment of the frame (Fig. 3, paragraph 43, wherein controller 340 may include a timing controller; Fig. 6, paragraph 66-67, wherein controller 340 controls and coordinates components in a super resolution engine for upscaling images, which is interpreted as receiving an input image frame, including the first segment of the image frame); and implement the machine-learning-based algorithm to upscale the first segment of the frame while receiving a second segment of the frame (Fig. 6, paragraph 80, wherein neural network 612 upscales enhanced image data which is interpreted as a first segment of the frame; Fig. 6, paragraph 84-85, wherein feature detection processor 604 determines features from the image, which includes segmenting the image data, which is interpreted as receiving a second segment of the frame; Fig. 6 is interpreted as showing the neural network and feature detection processor working in parallel).
The motivation to combine would be the same as that set forth for claim 1.
Regarding claim 16, Kim teaches one or more non-transitory computer-readable media comprising a plurality of instructions stored thereon that, when executed, causes a compute device to: implement, by a display module, a machine-learning-based algorithm to upscale the frame to generate an upscaled frame (Fig. 10, Col. 25 line 43 – Col. 26 line 3, wherein learning processor DRL is defined as a deep reinforcement learning processor and interpreted as a machine-learning-based algorithm, which is used to increase a resolution of an on screen display to a greater resolution to generate an output image, which is interpreted as upscaling a frame; Fig. 2, Col. 5 line 61 – Col. 6 line 11, wherein the image display apparatus 100 is interpreted as a display module, and contains signal processor 170); and display the upscaled frame on a display panel of the display module (Fig. 10, Col. 25 lines 36-42, wherein synthesized upscaled image is outputted to display 180; Fig. 2, Col. 5 line 61 – Col. 6 line 11, wherein the display 180 is a display panel of the image display apparatus 100).
Kim does not teach a display module; and display controller circuitry to: generate a frame; and send the frame to the display module.
Chou teaches a display module; and display controller circuitry (Fig. 2, paragraph 34, display and display controller circuitry to send image data to the display) to: generate a frame; and send the frame to the display module (Fig. 3, paragraph 40-41, wherein the display controller may include an image data processing pipeline and can be a display pipeline, which is interpreted as sending image data to the display module; paragraph 45, wherein the image data may be processed and stored in a frame buffer, which is interpreted as generating a frame to send to the display module).
The motivation to combine would be the same as that set forth for claim 1.
7. Claims 2, 5, 11, 14, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Chou, and further in view of Ki (US 20240223917 A1), hereinafter Ki.
Regarding claim 2, Kim in view of Chou discloses the display module of claim 1. Additionally, Ki teaches the display module of claim 1, wherein the machine-learning-based algorithm comprises a convolutional neural network (Fig. 1, paragraph 53, wherein as part of the zoom-in process for upscaling an image neural networks are implemented; paragraph 90, wherein neural network may be a deep convolutional network).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kim with the teachings of Ki for this display module for implementing a machine-learning-based algorithm to upscale a frame. Kim discloses an image display device configured to upscale content from a first resolution to a greater resolution, utilizing machine learning to adjust the luminance and transparency of the content as part of outputting the upscaled content. Similarly, Chou discusses an engine for converting a lower resolution input image to a higher resolution output image that involves using a neural network to upscale the luminance channel and color channels of the input data. Additionally, Ki discloses a method for generating high resolution images involving using machine learning to upscale the luminance and chrominance components of the image as part of the process of outputting the upscaling image. All three references disclose similar methods for using machine learning in order to upscaling images. As all three references teach analogous art, it would be obvious to have combined them.
Regarding claim 5, Kim in view of Chou discloses the display module of claim 1. Additionally, Ki teaches upscaling a luminance channel of the frame using a convolutional neural network to generate an upscaled luminance frame (Fig. 1, paragraph 66, wherein obtaining high-res texture information by upscaling luminance information of an input image is interpreted as generating an upscaled luminance frame); upscaling a plurality of color channels of the frame to generate an upscaled chrominance frame (Fig. 1, paragraph 67, wherein obtaining high res color information by upscaling color information based on features of the input RGB image is interpreted as obtaining an upscaled chrominance frame; paragraph 55, wherein features of the input RGB image includes the chrominance components); and combining the upscaled luminance frame and the upscaled chrominance frame to generate the upscaled frame (Fig. 1, paragraph 68, wherein generating a high-res RGB image by concatenating high-res color information and high-res texture information is interpreted as generating the upscaled frame).
The motivation to combine would be the same as that set forth for claim 2.
Regarding claim 11, Kim in view of Chou discloses the compute device of claim 7. Additionally, Ki teaches the compute device of claim 7, wherein the machine-learning-based algorithm comprises a convolutional neural network (Fig. 1, paragraph 53, wherein as part of the zoom-in process for upscaling an image neural networks are implemented; paragraph 90, wherein neural network may be a deep convolutional network).
The motivation to combine would be the same as that set forth for claim 2.
Regarding claim 14, Kim in view of Chou discloses the compute device of claim 7. Additionally, Ki teaches upscaling a luminance channel of the frame using a convolutional neural network to generate an upscaled luminance frame (Fig. 1, paragraph 66, wherein obtaining high-res texture information by upscaling luminance information of an input image is interpreted as generating an upscaled luminance frame); upscaling a plurality of color channels of the frame to generate an upscaled chrominance frame (Fig. 1, paragraph 67, wherein obtaining high res color information by upscaling color information based on features of the input RGB image is interpreted as obtaining an upscaled chrominance frame; paragraph 55, wherein features of the input RGB image includes the chrominance components); and combining the upscaled luminance frame and the upscaled chrominance frame to generate the upscaled frame (Fig. 1, paragraph 68, wherein generating a high-res RGB image by concatenating high-res color information and high-res texture information is interpreted as generating the upscaled frame).
The motivation to combine would be the same as that set forth for claim 2.
Regarding claim 17, Kim in view of Chou discloses the computer readable media of claim 16. Additionally, Ki teaches upscaling a luminance channel of the frame using a convolutional neural network to generate an upscaled luminance frame (Fig. 1, paragraph 66, wherein obtaining high-res texture information by upscaling luminance information of an input image is interpreted as generating an upscaled luminance frame); upscaling a plurality of color channels of the frame to generate an upscaled chrominance frame (Fig. 1, paragraph 67, wherein obtaining high res color information by upscaling color information based on features of the input RGB image is interpreted as obtaining an upscaled chrominance frame; paragraph 55, wherein features of the input RGB image includes the chrominance components); and combining the upscaled luminance frame and the upscaled chrominance frame to generate the upscaled frame (Fig. 1, paragraph 68, wherein generating a high-res RGB image by concatenating high-res color information and high-res texture information is interpreted as generating the upscaled frame).
The motivation to combine would be the same as that set forth for claim 2.
8. Claims 3, 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Chou, and further in view of Lee (US 11715410 B2), hereinafter Lee '410, and Croxford (US 10984758 B1), hereinafter Croxford.
Regarding claim 3, Kim discloses the display module of claim 1. Additionally, Lee ‘410 teaches upscaling frames from a resolution of about 1920 x 1080 to a resolution of about 3840 x 2160 (Col. 10, lines 53-59, wherein content can be upscaled to a resolution of 3840 x 2160) at about 60 frames per second (Col. 17, lines 10-17, wherein the resolution of 1920 x 1080 content may be upscaled to a higher resolution while matching a 60 Hz display panel, which is interpreted as being able to upscale it to a higher resolution including 3840 x 2160 at about 60 frames per second).
Neither Kim, Chou, and Lee ‘410 do not teach upscaling frames using less than about 150 milliwatts of power.
Croxford teaches upscaling frames using less than about 150 milliwatts of power (Fig. 3, Col. 13 lines 9-37, wherein while sending enhanced data to a display controller as part of the upscaling process, the display controller can reduce power consumption, which suggests upscaling using less power which can include using less than 150 milliwatts of power).
It would be obvious before the effective filing date of the claimed invention to have modified Kim with the teachings of Lee ‘410 and Croxford for this display module for upscaling frames. Kim discloses an image display device configured to upscale content from a first resolution to a greater resolution by utilizing machine learning. Similarly, Chou also discusses an engine for converting a lower resolution input image to a higher resolution output image using a neural network. Lee ‘410 discusses a display apparatus configured to adjust the resolution and framerate of received content, and displaying the content at an adjusted resolution and framerate, which includes upscaling the content’s resolution. Similarly, Croxford discusses a display controller utilizing machine learning in order to upscale input image frames and increase their resolutions. Both Kim, Chou, and Croxford discuss using machine learning in order to upscale content such as images, while Kim and Lee ‘410 both discuss display devices capable of upscaling the resolution of their input content and outputting the upscaled content to a display. As all four references discuss methods of upscaling content and outputting them to a display, it would be obvious to combine them.
Regarding claim 12, Kim discloses the compute device of claim 7. Additionally, Lee ‘410 teaches upscaling frames from a resolution of about 1920 x 1080 to a resolution of about 3840 x 2160 (Col. 10, lines 53-59, wherein content can be upscaled to a resolution of 3840 x 2160) at about 60 frames per second (Col. 17, lines 10-17, wherein the resolution of 1920 x 1080 content may be upscaled to a higher resolution while matching a 60 Hz display panel, which is interpreted as being able to upscale it to a higher resolution including 3840 x 2160 at about 60 frames per second).
Neither Kim, Chou, and Lee ‘410 do not teach upscaling frames using less than about 150 milliwatts of power.
Croxford teaches upscaling frames using less than about 150 milliwatts of power (Fig. 3, Col. 13 lines 9-37, wherein while sending enhanced data to a display controller as part of the upscaling process, the display controller can reduce power consumption, which suggests upscaling using less power which can include using less than 150 milliwatts of power).
The motivation to combine would be the same as that set forth for claim 3.
9. Claims 6, 15, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Chou and Ki as applied to claims 5, 14, 17 above, and further in view of Jung (US 20200311870 A1), hereinafter Jung.
Regarding claim 6, Kim in view of Chou and Ki discloses the display module of claim 5. Additionally, Jung teaches upscaling the plurality of color channels of the frame to generate the upscaled chrominance frame without use of a neural network (Fig. 3, paragraph 66-67, wherein the chrominance component is upscaled using bi-cubic interpolation, which is interpreted as not using a neural network).
It would be obvious to one of ordinary skill before the effective filing date of the claimed invention to have modified Kim in view of Chou and Ki to incorporate the teachings of Jung for this display module for upscaling a frame. Kim discloses an image display device configured to upscale content from a first resolution to a greater resolution, utilizing machine learning to adjust the luminance and transparency of the content as part of outputting the upscaled content. Similarly, Chou discusses an engine for converting a lower resolution input image to a higher resolution output image that involves using a neural network to upscale the luminance channel and color channels of the input data. Furthermore, Ki discloses a method for generating high resolution images involving using machine learning to upscale the luminance and chrominance components of the image as part of the process of outputting the upscaling image. Likewise, Jung also discusses a method for upscaling the resolution of input data, involving using a neural network for upscaling the luminance component. Both Kim, Chou, Ki, and Jung disclose similar methods for upscaling images, including utilizing machine learning as part of the process, and directly upscaling the luminance components of the image. Because of this, it would be obvious to combine these four references.
Regarding claim 15, Kim in view of Ki disclose the compute device of claim 14. Additionally, Jung teaches upscaling the plurality of color channels of the frame to generate the upscaled chrominance frame without use of a neural network (Fig. 3, paragraph 66-67, wherein the chrominance component is upscaled using bi-cubic interpolation, which is interpreted as not using a neural network).
The motivation to combine would be the same as that set forth for claim 6.
Regarding claim 18, Kim in view of Ki discloses the non-transitory computer-readable media of claim 17. Additionally, Jung teaches upscaling the plurality of color channels of the frame to generate the upscaled chrominance frame without use of a neural network (Fig. 3, paragraph 66-67, wherein the chrominance component is upscaled using bi-cubic interpolation, which is interpreted as not using a neural network).
The motivation to combine would be the same as that set forth for claim 6.
10. Claims 9, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Chou, and further in view of Croxford.
Regarding claim 9, Kim in view of Chou discloses the compute device of claim 7. Additionally, Kim teaches the compute device of claim 7, wherein the display controller is further to: determine, prior to generation of the frame, to begin implementation of upscaling (Col. 5, lines 50-60, wherein the image display apparatus upscales the resolution of the on-screen display before synthesizing the final upscaled output image, which suggest upscaling is implemented prior to generation of the frame).
Neither Kim nor Chou teaches changing, in response to the determination to begin implementation of upscaling, a bandwidth of a link between the display module and another component of the compute device.
Croxford teaches changing, in response to the determination to begin implementation of upscaling, a bandwidth of a link between the display module and another component of the compute device (Fig. 3, Col. 13 lines 9-37, wherein while sending enhanced data to a display controller as part of the upscaling process, the display controller can reduce the bandwidth required, which suggests decreasing the bandwidth in response to upscaling frame data).
It would be obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Kim with the teachings of Croxford for this compute device for upscaling a frame. Kim discloses an image display device configured to upscale content from a first resolution to a greater resolution by utilizing machine learning. Additionally, Chou discusses converting a lower resolution input image to a higher resolution output image using a neural network to upscale the input data. Similarly, Croxford discusses a display controller utilizing machine learning in order to upscale input image frames and increase their resolutions. As all three references discuss analogous art and utilize machine learning for their process of upscaling the resolution of input data, it would be obvious to combine them.
Regarding claim 19, Kim discloses the non-transitory computer-readable media of claim 16. Additionally, Kim teaches the non-transitory computer-readable media of claim 16, wherein the plurality of instructions further causes the compute device to: determine, prior to generation of the frame, to begin implementation of upscaling (Col. 5, lines 50-60, wherein the image display apparatus upscales the resolution of the on-screen display before synthesizing the final upscaled output image, which suggest upscaling is implemented prior to generation of the frame).
Kim does not teach changing, in response to the determination to begin implementation of upscaling, a bandwidth of a link between the display module and another component of the compute device.
Croxford teaches changing, in response to the determination to begin implementation of upscaling, a bandwidth of a link between the display module and another component of the compute device (Fig. 3, Col. 13 lines 9-37, wherein while sending enhanced data to a display controller as part of the upscaling process, the display controller can reduce the bandwidth required, which suggests decreasing the bandwidth in response to upscaling frame data).
The motivation to combine would be the same as that set forth for claim 9.
11. Claims 10, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Chou and Croxford as applied to claims 9, 19 above, and further in view of Lee (US 20240303767 A1), hereinafter Lee '767.
Regarding claim 10, Kim in view of Chou and Croxford disclose the compute device of claim 9. Additionally, Kim teaches the compute device of claim 9, wherein the display controller is further to: change, in response to the determination to begin implementation of upscaling, a resolution of frames to be sent to the display module (Figs 2-3, Col. 9, line 41 – Col. 10, line 9, wherein the resolution of the decoded image signal is output by the scaler 335 to the display 180).
Neither Kim, Chou, or Croxford teaches changing, in response to the determination to begin implementation of upscaling, a dots per inch setting of the compute device.
Lee ‘767 teaches changing, in response to the determination to begin implementation of upscaling, a dots per inch setting of the compute device (Fig. 7, paragraph 103, wherein when a given scaler is selected, interpreted as determining to begin implementation of upscaling, the electronic device may control the DPI to upscale the image, which is interpreted as changing the dots per inch setting of the compute device).
It would be obvious to one of ordinary skill before the effective filing date of the claimed invention to have modified Kim in view of Chou and Croxford with the teachings of Lee ‘767 for this compute device for upscaling frames. Kim discloses an image display device configured to upscale content from a first resolution to a greater resolution by utilizing machine learning. Additionally, Chou discusses converting a lower resolution input image to a higher resolution output image that involves using a neural network to upscale the input data. Similarly, Croxford discusses a display controller utilizing machine learning in order to upscale input image frames and increase their resolutions. Furthermore, Lee ‘767 also discusses using machine learning in the process of upscaling an image, determining a scaler algorithm to upscale the image and output it to a display. Both Kim, Chou, Croxford, and Lee ‘767 discuss using machine learning in order to upscale content such as images, and outputting the upscaled content to a display. As all four references discuss analogous art for utilizing machine learning for upscaling input data, it would be obvious to combine them.
Regarding claim 20, Kim in view of Croxford disclose the non-transitory computer-readable media of claim 19. Additionally, Kim teaches the non-transitory computer-readable media of claim 19, wherein the plurality of instructions further causes the compute device to: change, in response to the determination to begin implementation of upscaling, a resolution of frames to be sent to the display module (Figs 2-3, Col. 9, line 41 – Col. 10, line 9, wherein the resolution of the decoded image signal is output by the scaler 335 to the display 180).
Kim does not teach changing, in response to the determination to begin implementation of upscaling, a dots per inch setting of the compute device.
Lee ‘767 teaches changing, in response to the determination to begin implementation of upscaling, a dots per inch setting of the compute device (Fig. 7, paragraph 103, wherein when a given scaler is selected, interpreted as determining to begin implementation of upscaling, the electronic device may control the DPI to upscale the image, which is interpreted as changing the dots per inch setting of the compute device).
The motivation to combine would be the same as that set forth for claim 10.
Response to Arguments
12. Applicant’s arguments, see p. 8-11 of the attorney's remarks, filed January 2nd, 2026, with respect to the rejections of claims 1, 7, and 16 under 35 U.S.C. 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Kim (US 12322359 B2) and Chou (US 20200043135 A1) under 35 U.S.C. 103
Conclusion
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN W YICK whose telephone number is (571)272-4063. The examiner can normally be reached M-F 8-5.
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/JORDAN WAN YICK/Examiner, Art Unit 2612
/Said Broome/Supervisory Patent Examiner, Art Unit 2612
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