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 § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, 7, 9, 10, 12, 13,15, and 17 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by K S et al (US 2022/0398700; hereinafter K S).
Regarding claim 1:
K S discloses a head-mounted night vision system (see Fig. 1 and paragraph [0057]) comprising:
a sensor assembly configured to capture RAW video data of a scene, the RAW video data including a RAW image frame and an immediately prior RAW image frame (see Fig. 1; camera 106 and paragraph [0016]; the camera receives a media stream which includes raw video data of an image frame and an immediately prior image frame);
a neural processing unit configured to:
align an encoded version of the RAW image frame with an encoded version of the immediately prior RAW image frame to form an aligned encoded image frame (see Fig. 4 and paragraphs [0021] and [0072]-[0074]; “analyzing each frame with respect to earlier frames to determine whether the shot boundary detection is associated with each of the plurality of frames” and “the controller 110 performs an alignment of a plurality of frames of the media stream”); also see , and
apply at least a portion of the aligned encoded image frame and a latent frame history to a night vision model to generate an enhanced image frame of the scene (see Fig. 4 and paragraphs [0099]-[0103] and [0138]); and
a display assembly that includes a display configured to present enhanced video data of the scene based that is based in part on a plurality of enhanced image frames including the enhanced image frame (see Fig. 4 and paragraph [0085]).
Regarding claim 2:
K S discloses the head-mounted night vision system of claim 1, wherein the night vision model is a recurrent neural network (see paragraph [0064]).
Regarding claim 7:
K S discloses the head-mounted night vision system of claim 1, wherein the neural processing unit is configured to directly receive the RAW video data from the sensor assembly (see Figs. 1-2; the raw video data is directly inputted into memory 102 and controller 110 which are interpret3e as the neural processing unit).
Regarding claim 9:
K S discloses the head-mounted night vision system of claim 1, wherein the sensor assembly comprises: a low light sensor that is configured to capture the RAW video data of the scene (see paragraph [0036]).
Regarding claim 10:
K S discloses the head-mounted night vision system of claim 9, wherein the low light sensor that is a color sensor and the enhanced video data is in color (see paragraph [0017]; the media stream includes color artifacts which is video data in color).
Regarding claims 12, 13, 15 and 17:
Claims 12, 13, 15 and 17 recite similar limitations as in claims 1, 2, 7 and 9. Hence, claims 12, 13, 15, and 17 are rejected under the same reasons as discussed above in claims 1, 2, 7, and 9, respectively.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent 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) 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over K S in view of (Price et al. 2022/0385842; hereinafter Price).
Regarding claim 4:
K S discloses all the features in claim 1. However, K S does not disclose the head-mounted night vision system, further comprising:
an inertial measurement data configured to provide positional data of the head-mounted night vision system, wherein the neural processing unit is configured to use the positional data to align the encoded version of the RAW image frame with the encoded version of the immediately prior RAW image frame.
In the same field of endeavor, Price discloses a head-mounted night vision system (see Fig. 1 and paragraph [0045]), comprising:
an inertial measurement data configured to provide positional data of the head-mounted night vision system (see Fig. 1, IMU 114), wherein the neural processing unit is configured to use the positional data to align the encoded version of the RAW image frame with the encoded version of the immediately prior RAW image frame (see paragraph [0110]; “generating an aligned preceding image frame by using motion data associated with the SPAD array to spatially align the preceding image frame with the image frame”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the teaching of K S and Price to further include an inertial measurement data configured to provide positional data of the head-mounted night vision system, wherein the neural processing unit is configured to use the positional data to align the encoded version of the RAW image frame with the encoded version of the immediately prior RAW image frame. The combination would have yielded a predictable result of providing a display device with reduced noise and improved signal (see Price, paragraph [0078]).
Regarding claim 14:
Claim 14 recites similar limitations as in claim 4. Hence, claim 14 is rejected under the same reason as discussed above in claim 4.
Claim(s) 5, 8, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over K S in view of Nishide et al. (US 11,579,688; hereinafter Nishide).
Regarding claim 5:
K S discloses all the features in claim 1. K S does not disclose the head-mounted night vision system, further comprising:
an eye tracker assembly configured to determine eye tracking information of an eye within an eyebox of the head-mounted night vision system;
wherein the neural processing unit is configured to:
adjust a spatial resolution of the aligned encoded image frame based in part on the eye tracking information to have variable spatial resolution, wherein an image frame with variable spatial resolution has a first resolution for a first region of the image frame that is higher than a second resolution of a peripheral region that is outside the first region, wherein a location of the first region corresponds to a gaze location of the eye, and
apply the aligned encoded image frame with the variable spatial resolution and the latent frame history to the night vision model to generate the enhanced image frame of the scene, wherein the enhanced image frame of the scene has variable spatial resolution.
However, in the same field of endeavor, Nishide discloses a head-mounted night vision system (see col. 16, lines 39-48), comprising:
an eye tracker assembly configured to determine eye tracking information of an eye within an eyebox of the head-mounted night vision system (see Fig. 1A; pupil detection unit 104 and col. 8, lines 46-51);
wherein the neural processing unit (see Fig. 5A; AI unit 107 and col. 9, line 62 to col. 10, line 28) is configured to:
adjust a spatial resolution of the aligned encoded image frame based in part on the eye tracking information to have variable spatial resolution, wherein an image frame with variable spatial resolution has a first resolution for a first region of the image frame that is higher than a second resolution of a peripheral region that is outside the first region, wherein a location of the first region corresponds to a gaze location of the eye (see col. 11, lines 18-45)and
apply the aligned encoded image frame with the variable spatial resolution and the latent frame history to the night vision model to generate the enhanced image frame of the scene, wherein the enhanced image frame of the scene has variable spatial resolution (see col. 11, lines 39-42; “reducing a processing amount, there are a method of decreasing resolution of a portion other than a region of interest” suggests variable spatial resolution).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the teaching of K S and Nishide to further include an eye tracker assembly configured to determine eye tracking information of an eye within an eyebox of the head-mounted night vision system; wherein the neural processing unit is configured to: adjust a spatial resolution of the aligned encoded image frame based in part on the eye tracking information to have variable spatial resolution, wherein an image frame with variable spatial resolution has a first resolution for a first region of the image frame that is higher than a second resolution of a peripheral region that is outside the first region, wherein a location of the first region corresponds to a gaze location of the eye, and apply the aligned encoded image frame with the variable spatial resolution and the latent frame history to the night vision model to generate the enhanced image frame of the scene, wherein the enhanced image frame of the scene has variable spatial resolution. The combination would have yielded a predictable result of reducing the load of the data processing amount in the head-mounted display device (see Nishide, col. 11, lines 39-40).
Regarding claim 8:
K S discloses all the features in claim 1. K S does not disclose the head-mounted night vision system, wherein a latency between capturing the RAW video data and presenting the enhanced video data is below 10 milliseconds.
In the same field of endeavor, Nishide discloses a head-mounted night vision system, wherein a latency between capturing the RAW video data and presenting the enhanced video data is below 10 milliseconds (see col. 13, lines 10-14; “a time taken for one image (one frame) from when the image is captured to when the image is displayed is about 4×10.sup.−3 (seconds)”).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the teaching of K S and Nishide such that a latency between capturing the RAW video data and presenting the enhanced video data is below 10 milliseconds. The combination would have yielded a predictable result of reducing the latency between the capturing the video data and presenting the enhanced video data.
Regarding claim 16:
Claim 16 recites similar limitation as in claim 8. Hence, claim 16 is rejected under the same reason as discussed above in claim 8.
Claim(s) 11 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over K S, as applied in claim 1 above, and further in view of Abuolaim et al. (US 12,015,855; hereinafter Abuolaim), and further in view Ding et al. (WO 2023/177388; hereinafter Ding).
Regarding claim 11:
K S discloses all the features in claim 1. K S does not disclose the head-mounted night vision system wherein the night vision model was formed by: generating training image pairs using low noise video data, wherein each training image pair includes a low noise image frame and a noisy image frame that was generated in part using the low noise image frame; training a foundation model using the training image pairs; distilling the foundation model to form a small model; performing quantization on the small model; and performing quantization aware training on the small model to form the night vision model.
In the same field of endeavor, Abuolaim discloses a system wherein the night vision model was formed by:
generating training image pairs using low noise video data, wherein each training image pair includes a low noise image frame and a noisy image frame that was generated in part using the low noise image frame (see Fig. 4 and col. 5, lines 21-29); and
training a foundation model using the training image pairs (see col. 5, liens 13-20).
In addition, Ding discloses a system, wherein the night vision model was formed by:
distilling the foundation model to form a small model (see paragraph [0003]; based on the broadest reasonable interpretation, “deep learning techniques rely on a sequence of neural networks” reads on “distilling the foundation model to form a small model”);
performing quantization on the small model (see paragraph [0055]); and
performing quantization aware training on the small model to form the night vision model (see paragraphs [0003] and [0055]).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the teaching of K S, Abuolaim, and Ding such that the night vision model is formed by: generating training image pairs using low noise video data, wherein each training image pair includes a low noise image frame and a noisy image frame that was generated in part using the low noise image frame; training a foundation model using the training image pairs; distilling the foundation model to form a small model; performing quantization on the small model; and performing quantization aware training on the small model to form the night vision model. The combination would have yielded a predictable result of real-time denoising of video content under low illumination conditions can be realized (see Ding, paragraph [0003]).
Regarding claim 18:
Claim 18 recites similar limitation as in claim 11. Hence, claim 18 is rejected under the same reason as discussed above in claim 11.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Abuolaim et al. (US 12,015,855; hereinafter Abuolaim), and further in view Ding et al. (WO 2023/177388; hereinafter Ding).
Regarding claim 19:
Abuolaim discloses a method, performed at a computer system comprising a processor and a computer-readable medium, comprising:
generating training image pairs using low noise video data, wherein each training image pair includes a low noise image frame and a noisy image frame that was generated in part using the low noise image frame (see Fig. 4 and col. 5, lines 21-29); and
training a foundation model using the training image pairs (see col. 5, liens 13-20).
Abuolaim dose not disclose distilling the foundation model to form a small model; performing quantization on the small model; and performing quantization aware training on the small model to form the night vision model.
In the same field of endeavor, Ding discloses a system, wherein the night vision model was formed by:
distilling the foundation model to form a small model (see paragraph [0003]; based on the broadest reasonable interpretation, “deep learning techniques rely on a sequence of neural networks” reads on “as distilling the foundation model to form a small model”);
performing quantization on the small model (see paragraph [0055]); and
performing quantization aware training on the small model to form the night vision model (see paragraphs [0003] and [0055]).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the teaching of Abuolaim, and Ding such that the night vision model is formed by: generating training image pairs using low noise video data, wherein each training image pair includes a low noise image frame and a noisy image frame that was generated in part using the low noise image frame; training a foundation model using the training image pairs; distilling the foundation model to form a small model; performing quantization on the small model; and performing quantization aware training on the small model to form the night vision model. The combination would have yielded a predictable result of real-time denoising of video content under low illumination conditions can be realized (see Ding, paragraph [0003]).
Allowable Subject Matter
Claims 3, 6 and 20 are 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.
In regards to claim 3, none of the reference of record alone or in combination discloses or suggests the head-mounted night vision system of claim 1, wherein the RAW Video data includes a first data stream of RAW image frames from a first channel and a second data stream of RAW image frames from a second channel, and the first data stream includes the RAW image frame and the immediately prior RAW image frame, and the second data stream includes a second RAW image frame and a second immediately prior RAW image frame, and the neural processing unit is further configured to: align a second encoded version of the second RAW image frame with an encoded version of the second immediately prior RAW image frame to form a second aligned encoded image frame; and apply at least a portion of the aligned encoded image frame, at least a portion of the second aligned encoded image frame and the latent frame history to the night vision model to generate the enhanced image frame of the scene and a second enhanced image frame of the scene, wherein the night vision model uses both the aligned encoded image frame and the second aligned encoded image frame in the generation of the enhanced image frame and the generation of the second enhanced image frame, wherein the display assembly further comprises: a second display configured to present a second enhanced video data of the scene that is based in part on a second plurality of enhanced image frames including the second enhanced image frame, and the first display is configured to provide the enhanced video data to a left eyebox of the head-mounted night vision system and the second display is configured to provide the second enhanced video data to a right eyebox of the head-mounted night vision system.
In regards to claim 6, none of the reference of record alone or in combination discloses or suggests the head-mounted night vision system of claim 1, further comprising: an eye tracker assembly configured to determine eye tracking information of an eye within an eyebox of the head-mounted night vision system; wherein the neural processing unit is configured to: apply the aligned encoded image frame, the eye tracking information, and the latent frame history to the night vision model to generate the enhanced image frame of the scene, wherein the enhanced image frame of the scene has variable spatial resolution such that the enhanced image frame has a first resolution for a first region of the enhanced image frame that is higher than a second resolution of a peripheral region that is outside the first region, wherein a location of the first region corresponds to a gaze location of the eye.
In regards to claim 20, none of the reference of record alone or in combination discloses or suggests the method of claim 19, wherein generating the training image pairs using the low noise video data, comprises: determining readout noise of a sensor of a digital night vision device based in part on a plurality of calibration frames associated with the sensor; estimating shot noise of the sensor; generating noisy image frames using the readout noise, the shot noise, and low noise image frames of the low noise video data; and generating training image pairs using the noisy image frames and the low noise image frames.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Varna et al. (US 2025/0071412) discloses a method and system for enhancing low light frame in a multi-camera system.
Patel et al. (US 2024/0394838) discloses using neural network to improve image processing for low-light images.
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/LIXI C SIMPSON/Primary Examiner, Art Unit 2625
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