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 .
DETAILED ACTION
Priority
Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement(s) submitted on 6/5/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1 and 5-9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pezeshkian (US 9175930 B1).
Regarding claim 1, Pezeshkian teaches An electronic paper display system for a surface of equipment (col. 5, line 21; “FIGS. 10A and 10B show an embodiment of the camouflaged robot concept with AEC capability. The gray panels represent the e-paper display”), comprising:
an electronic paper display panel conformally covering the surface of the equipment and configured to display at least one color (Figs. 10-17);
an image capturing device configured to capture an environment at which the equipment is located for generating an ambient image (col. 4, line 58; “the robot takes an image of the environment and then renders a predefined camouflage pattern with the appropriate colors chosen based on the input image (image taken by camera)”); and
a processor electrically connected to the image capturing device and the electronic paper display panel, and generating a first control signal according to the ambient image for the electronic paper display panel to change the at least one color displayed on the surface of the equipment to be similar to an ambient color of the environment at which the equipment is located according to the first control signal (col. 6, line 15; “the AEC robot or sensor can adapt its surface colors and shapes according to the environment”).
Regarding claim 5, Pezeshkian teaches the electronic paper display system of claim 1, further comprising:
a driving circuit electrically connected between the processor and the electronic paper display panel and configured to generate a driving signal according to the first control signal for driving the electronic paper display panel to display the at least one color similar to the ambient color (Figs. 1, 17; col. 3, lines 24-34; col. 2, lines 5-25).
Regarding claim 6, Pezeshkian teaches the electronic paper display system of claim 1, wherein the electronic paper display panel comprises a plurality of display areas distributed on the surface of the equipment, and the processor is further configured to:
generate a plurality of second control signals corresponding to the plurality of display areas according to the ambient image for each of the plurality of display areas to display the at least one color to be similar to an adjacent ambient color according to the plurality of second control signals (Figs. 1, 17; col. 3, lines 24-34; col. 2, lines 5-25).
Regarding claim 7, Pezeshkian teaches the electronic paper display system of claim 1, wherein the image capturing device is a rotatable image capturing device, and is configured to capture a plurality of ambient images of the environment surrounding the equipment (col. 3, lines 57-60).
Regarding claim 8, Pezeshkian teaches the electronic paper display system of claim 1, further comprising:
a power supply system configured to provide power required by the electronic paper display panel, the image capturing device, and the processor (Figs. 10-17).
Regarding claim 9, Pezeshkian teaches the electronic paper display system of claim 1, wherein the electronic paper display panel is a segmented display comprising a plurality of segments (Figs. 10-17).
At least Claim(s) 1 and 7 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kwon et al (US 10642121 B2).
Regarding claim 1, Kwon teaches An electronic paper display system (Figs. 1, 7, 8) for a surface of equipment, comprising:
an electronic paper display panel (200) conformally covering the surface of the equipment and configured to display at least one color (Figs. 1, 7, 8; col. 14; “display unit 200 composed of multiple reflective display devices 100 surrounds the exterior of the combat vehicle… the display unit may cover the combat vehicle as a thin film”);
an image capturing device (300) configured to capture an environment at which the equipment is located for generating an ambient image (Figs. 1, 7, 8; col. 13, lines 53-67); and
a processor (400) electrically connected to the image capturing device and the electronic paper display panel, and generating a first control signal according to the ambient image for the electronic paper display panel to change the at least one color displayed on the surface of the equipment to be similar to an ambient color of the environment at which the equipment is located according to the first control signal (Figs. 1, 7, 8; col 14: “image controller 410 analyses the image obtained by the camera unit 300, forms a camouflage pattern hidden in the surrounding environment, and provides the formed camouflage pattern to the display unit 200”; col. 15: “pattern forming module 412 forms, based on the representative colors and distributions, a visible light camouflage pattern to be displayed on the display unit 200”).
Regarding claim 7, Kwon teaches the electronic paper display system of claim 1, wherein the image capturing device is a rotatable image capturing device, and is configured to capture a plurality of ambient images of the environment surrounding the equipment (col. 14, lines 34-40: “camera unit 300 may be provided as a plurality of cameras to photograph the surrounding environment in every direction, or may be provided in a rotatable structure to obtain images in every direction”).
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 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) 2-3, 10 and 14-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pezeshkian (US 9175930 B1) in view of Kinsman et al (US 20230059496 A1).
Regarding claim 10, Pezeshkian teaches An electronic paper display method applied on a surface of equipment, the surface of the equipment is covered with an electronic paper display panel for displaying at least one color (Figs. 10-17; col. 5, line 21; “FIGS. 10A and 10B show an embodiment of the camouflaged robot concept with AEC capability. The gray panels represent the e-paper display”; col. 4, line 58; “the robot takes an image of the environment and then renders a predefined camouflage pattern with the appropriate colors chosen based on the input image (image taken by camera)”), the electronic paper display method comprises:
obtaining an ambient image of an environment where the equipment is located (col. 4, line 58; “the robot takes an image of the environment and then renders a predefined camouflage pattern with the appropriate colors chosen based on the input image (image taken by camera)”);
changing the at least one color displayed on the electronic paper display panel to be similar to the ambient color according to the first control signal (col. 6, line 15; “the AEC robot or sensor can adapt its surface colors and shapes according to the environment”; col. 3, lines 3-10: “AEC is achieved by sampling the environment with a camera(s), performing image analysis to determine the proper colors and geometric shapes, and displaying the camouflage image on e-paper displays that shroud the outer surface of the item (robot, sensor, etc.) that is to be camouflaged”),
but fails to teach
training an artificial intelligence model through a color recognition algorithm for recognizing the ambient image to generate a first control signal corresponding to an ambient color of the environment where the equipment is located.
However, in the same field of endeavor Kinsman teaches
training an artificial intelligence model through a color recognition algorithm for recognizing the ambient image to generate a first control signal corresponding to an ambient color of the environment where the equipment is located (Figs. 3-4, 6; paras. 0051-0062; method 300 includes, training, by the data processing hardware 12, the machine learning model 130 with one or more camouflage patterns 132n having a higher rank; method 400 includes, by the data processing hardware 12, displaying one or more camouflage patterns 132n based on the ranking result. As discussed, camouflage patterns 132n having a higher rank are selected to be used in the intended environment 170; para. 0077: camouflage generator 105 updates camouflage material 132 (e.g., camouflage pattern 132n) for each of the displays 610-630 (dynamically) while the truck 602 is moving so that the camouflage material 132 is more relevant to the changing background (e.g., surrounding) ).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Kinsman in Pezeshkian to have training an artificial intelligence model through a color recognition algorithm for recognizing the ambient image to generate a first control signal corresponding to an ambient color of the environment where the equipment is located for training a camouflage pattern model for better results in real time generating active and adaptive camouflage patterns yielding a predicted result.
Regarding claim 14, the combination of Pezeshkianv and Kinsman teaches everything as claimed in claim 10. In addition, Pezeshkianv teaches
further comprising: capturing a plurality of ambient images of the environment surrounding the equipment (col. 3, lines 62-65: “Taking images from different areas around the object to be camouflaged provides a more comprehensive sample of the local environment. As such, the robot or sensor can then display different images on different sides of its body. This will allow it to be camouflaged better when looking at the robot or sensor from various angles”).
Regarding claim 15, the combination of Pezeshkianv and Kinsman teaches everything as claimed in claim 10. In addition, Pezeshkianv teaches
wherein the electronic paper display panel is a segmented display comprising a plurality of segments (Figs. 10-17).
Regarding claim 2, Pezeshkianv teaches everything as claimed in claim 1. In addition, Pezeshkianv teaches wherein the processor is further configured to
But fails to teach
wherein the processor is further configured to train an artificial intelligence model through a color recognition algorithm, enabling the artificial intelligence model to recognize the ambient color of the ambient image and generate the first control signal corresponding to the ambient color.
However, in the same field of endeavor Kinsman teaches
wherein the processor is further configured to train an artificial intelligence model through a color recognition algorithm, enabling the artificial intelligence model to recognize the ambient color of the ambient image and generate the first control signal corresponding to the ambient color (Figs. 3-4, 6; paras. 0051-0062; method 300 includes, training, by the data processing hardware 12, the machine learning model 130 with one or more camouflage patterns 132n having a higher rank; method 400 includes, by the data processing hardware 12, displaying one or more camouflage patterns 132n based on the ranking result. As discussed, camouflage patterns 132n having a higher rank are selected to be used in the intended environment 170; para. 0077: camouflage generator 105 updates camouflage material 132 (e.g., camouflage pattern 132n) for each of the displays 610-630 (dynamically) while the truck 602 is moving so that the camouflage material 132 is more relevant to the changing background (e.g., surrounding) ).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Kinsman in Pezeshkian to have wherein the processor is further configured to train an artificial intelligence model through a color recognition algorithm, enabling the artificial intelligence model to recognize the ambient color of the ambient image and generate the first control signal corresponding to the ambient color for training a camouflage pattern model for better results in real time generating active and adaptive camouflage patterns yielding a predicted result.
Regarding claim 3, the combination of Pezeshkianv and Kinsman teaches everything as claimed in claim 2. In addition, Pezeshkianv teaches
wherein the electronic paper display panel changes a pattern displayed on the surface of the equipment to be similar to an object outline of the environment at which the equipment is located according to the first control signal (Figs. 10-15; col. 4, line 58; “the robot takes an image of the environment and then renders a predefined camouflage pattern with the appropriate colors chosen based on the input image (image taken by camera)”; col. 3, lines 3-10: “AEC is achieved by sampling the environment with a camera(s), performing image analysis to determine the proper colors and geometric shapes, and displaying the camouflage image on e-paper displays that shroud the outer surface of the item (robot, sensor, etc.) that is to be camouflaged”).
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pezeshkian (US 9175930 B1) in view of Kinsman et al (US 20230059496 A1) as applied to claim 10, and further in view of Laycock et al (US 20110095692 A1).
Regarding claim 11, the combination of Pezeshkianv and Kinsman teaches everything as claimed in claim 10. In addition, Pezeshkianv teaches
wherein the electronic paper display panel comprises a plurality of display areas distributed on the surface of the equipment, and the electronic paper display method further comprises:
recognizing the plurality of image blocks in the ambient image
adjusting the at least one color corresponding to the plurality of display areas to be similar to an adjacent ambient color according to the plurality of second control signals (Figs. 10-15; col. 4, line 58; “the robot takes an image of the environment and then renders a predefined camouflage pattern with the appropriate colors chosen based on the input image (image taken by camera)”; col. 3, lines 3-10: “AEC is achieved by sampling the environment with a camera(s), performing image analysis to determine the proper colors and geometric shapes, and displaying the camouflage image on e-paper displays that shroud the outer surface of the item (robot, sensor, etc.) that is to be camouflaged”).
Moreover, Kinsman also teaches
wherein the electronic paper display panel comprises a plurality of display areas distributed on the surface of the equipment, and the electronic paper display method further comprises:
recognizing the plurality of image blocks in the ambient image through the artificial intelligence model to generate a plurality of second control signals corresponding to the plurality of display areas; and
adjusting the at least one color corresponding to the plurality of display areas to be similar to an adjacent ambient color according to the plurality of second control signals (Figs. 3-4, 6; paras. 0051-0062; method 300 includes, training, by the data processing hardware 12, the machine learning model 130 with one or more camouflage patterns 132n having a higher rank; method 400 includes, by the data processing hardware 12, displaying one or more camouflage patterns 132n based on the ranking result. As discussed, camouflage patterns 132n having a higher rank are selected to be used in the intended environment 170; para. 0077: camouflage generator 105 updates camouflage material 132 (e.g., camouflage pattern 132n) for each of the displays 610-630 (dynamically) while the truck 602 is moving so that the camouflage material 132 is more relevant to the changing background (e.g., surrounding)).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Kinsman in the combination to have those features for utilizing a trained camouflage pattern model for better results in real time generating active and adaptive camouflage patterns yielding a predicted result.
Furthermore, in the same field of endeavor Laycock teaches
wherein the electronic paper display panel comprises a plurality of display areas distributed on the surface of the equipment, and the electronic paper display method further comprises:
dividing the ambient image into a plurality of image blocks corresponding to the plurality of display areas (Laycock: Laycock: Figs. 6-7; para. 0034);
recognizing the plurality of image blocks in the ambient image through the artificial intelligence model (already taught by Kinsman above) to generate a plurality of second control signals corresponding to the plurality of display areas (Laycock: Figs. 6-7; para. 0034); and
adjusting the at least one color corresponding to the plurality of display areas to be similar to an adjacent ambient color according to the plurality of second control signals (Laycock: Figs. 6-7; para. 0034: “the input signals used to determine the intensity and colour of light emitted from the panels is provided by a colour video camera 720. The output of the camera 720 is received by a frame grabber 730 that derives a series of discrete frames from the camera output. The frames are processed by an image processor 740 that segments the captured image into a number of sub-images, there being one sub-image for each of the panels 100 in the array 710.”)
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Laycock in the combination to have these features for utilizing divided sub-image for generating camouflage pattern for each display panel so that optimized camouflage pattern per display panel can be obtained yielding a predicted result.
Regarding claim 12, the combination of Pezeshkianv, Kinsman and Laycock teaches everything as claimed in claim 11. In addition, Pezeshkianv teaches
further comprising: adjusting patterns displayed on the plurality of display areas to be similar to object outlines of the environment at which the equipment is located according to the plurality of second control signals (Figs. 10-15; col. 4, line 58; “the robot takes an image of the environment and then renders a predefined camouflage pattern with the appropriate colors chosen based on the input image (image taken by camera)”; col. 3, lines 3-10: “AEC is achieved by sampling the environment with a camera(s), performing image analysis to determine the proper colors and geometric shapes, and displaying the camouflage image on e-paper displays that shroud the outer surface of the item (robot, sensor, etc.) that is to be camouflaged”).
Claim(s) 4 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pezeshkian (US 9175930 B1) in view of Kinsman et al (US 20230059496 A1) as applied to claims 2 or 10, and further in view of Chun et al (US 20220164596 A1).
Regarding claim 13, the combination of Pezeshkianv and Kinsman teaches everything as claimed in claim 10. In addition, Kinsman teaches
wherein the color recognition algorithm includes Neural Networks (Fig. 1; para. 0023: “camouflage generator 105 includes a machine learning model 130 (e.g., neural network based model)”) for the same reason presented above in claim 10,
but fails to teach
wherein the color recognition algorithm includes Neural Networks, Region-based Color Analysis, and Color Difference Calculation.
However, in the same field of endeavor Chun teaches
wherein the color recognition algorithm includes Neural Networks, Region-based Color Analysis, and Color Difference Calculation (Figs. 3-5; paras. 0059-0063; “camouflage pattern evaluation module 220 analyzes the similarity between the operation environment image and the camouflage pattern image using an artificial intelligence-based camouflage performance evaluation algorithm.”, “camouflage pattern evaluation module 220 may scan the entire operation environment image using a sliding window technique and calculates each of the color similarity, pattern similarity, and structural similarity between the operation environment image and the camouflage pattern image for each sliding window”).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Chu in the combination to have wherein the color recognition algorithm includes Neural Networks, Region-based Color Analysis, and Color Difference Calculation for optimizing the camouflage pattern evaluation so that the best camouflage pattern can be utilized improving camouflage effects yielding a predicted result.
Regarding claim 4, claim 4 reciting features corresponding to claim 13 is also rejected for the same reason above.
Additional/Alternative Rejections
Sakamoto et al (US 20120200610 A1) can be used in combination with Pezeshkian to expressly teach claim 5: a driving circuit (13) electrically connected between the processor (15) and the electronic paper display panel (9) and configured to generate a driving signal according to the first control signal for driving the electronic paper display panel to display the at least one color similar to the ambient color (Fig. 15). Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Sakamoto in Pezeshkian to have the features for utilizing a display driving circuit for sending proper signals to display a proper camouflage image yielding a predicted result.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Quan Pham whose telephone number is (571)272-4438. The examiner can normally be reached Mon-Fri 9am-7pm.
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/Quan Pham/Primary Examiner, Art Unit 2637
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