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 § 112
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.
Claim 2 recites the limitation "a to be encoded image" which should read “the to be encoded image”, “wherein at least one first predetermined region” should read “wherein the at least one first predetermined region”.
Claim 12 is 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. Examiner is unclear from the wording if all of claim 1 is required for claim 12 or only the fused image from 1 for instance. Examiner recommends rewriting into independent format or rewording into more conventional dependent claim format.
Claim 21 is 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. Examiner is unclear from the wording if all of claim 1 is required for claim 21. Examiner recommends rewriting into independent format or rewording into more conventional dependent claim format.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 12, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rao et al. (US 20210012093 A1).
Regarding claim 1, Rao et al. disclose a method of processing an image, comprising: encoding a first to-be-encoded information corresponding to a display apparatus, so as to obtain a first encoded information (pose encoded images of the face image, [0018]); adjusting a pixel value of at least one first predetermined region of a to-be-encoded image according to the first encoded information, so as to obtain a first encoded image, wherein the to-be-encoded image corresponds to a target image (pose encoded images of the target face image, [0018], “In a possible implementation, the performing pose encoding on the face image based on two or more landmarks in the face image, to obtain pose encoded images of the face image includes: detecting the face image by using a landmark detection algorithm, to obtain location coordinates respectively corresponding to N landmarks of the face image, where N is an integer greater than 1; constructing N first images having a same size as the face image, where the N first images are in a one-to-one correspondence with the N landmarks; and performing, by using each of the N landmarks as a center, Gaussian blurring on the first image that is in the one-to-one correspondence with the landmark, to obtain N first Gaussian blurred images, where the N first Gaussian blurred images are the pose encoded images of the face image. In this possible implementation, the N landmarks of the face image are first determined, and then Gaussian blurring is performed, by using each landmark as a center, on the first image corresponding to the landmark. In the manner in which the image pose encoding is implemented by performing Gaussian blurring by using the landmark, descriptions of a face pose are more accurate and robust, so that a higher-quality face rotation image is obtained”, [0019]-[0023]) [blurring and rotation interpreted as adjusting pixel value]; and fusing the target image with the first encoded image to obtain a fused image (It should be noted that in the method according to the first aspect, the generating a to-be-input signal based on the face image, the pose encoded images of the face image, and the pose encoded images of the target face image may be specifically obtaining the to-be-input signal by fusing the face image, the pose encoded images of the face image, and the pose encoded images of the target face image in a feature fusion manner, [0018], “It should be noted that in the method 500, the generating a to-be-input signal based on the face image, the pose encoded images of the face image, and the pose encoded images of the target face image may be specifically obtaining the to-be-input signal by fusing the face image, the pose encoded images of the face image, and the pose encoded images of the target face image in a feature fusion manner”, [0270]).
Rao et al. do not disclose the term “adjusting pixel value”. It would have been obvious at the time of filing to one of ordinary skill in the art the blurring and rotation operations can be interpreted as adjusting pixel value as the operations are performed on an image and images are composed of pixels.
Regarding claim 12, Rao et al. disclose the method according to claim 1. Rao et al. further indicate a method of processing an image, comprising: processing a to-be-processed image to obtain a second encoded image, wherein the to-be-processed image comprises one of: a fused image or a captured image corresponding to the fused image, and the fused image is obtained by the method according to claim 1; obtaining a second encoded information according to a pixel value of at least one second predetermined region of the second encoded image; and decoding the second encoded information to obtain a second to-be-encoded information corresponding to a display apparatus (performing, by using each of the N landmarks as a center, Gaussian blurring on the first image that is in the one-to-one correspondence with the landmark, to obtain N first Gaussian blurred images, where the N first Gaussian blurred images are the pose encoded images of the face image, [0022], In this possible implementation, the N landmarks of the face image are first determined, and then Gaussian blurring is performed, by using each landmark as a center, on the first image corresponding to the landmark. In the manner in which the image pose encoding is implemented by performing Gaussian blurring by using the landmark, descriptions of a face pose are more accurate and robust, so that a higher-quality face rotation image is obtained, [0023], performing, by using each of the N landmarks as a center, Gaussian blurring on the first image that is in the one-to-one correspondence with the landmark, [0027], performing, by using each of the M landmarks as a center, Gaussian blurring on the second image that is in the one-to-one correspondence with the landmark, to obtain M second Gaussian blurred images, where the M second Gaussian blurred images are the pose encoded images of the target face image, [0032], detecting the face rotation image by using the landmark detection algorithm, to obtain location coordinates respectively corresponding to M landmarks of the face rotation image; constructing M second images having a same size as the face rotation image, where the M second images are in a one-to-one correspondence with the M landmarks; and performing, by using each of the M landmarks as a center, Gaussian blurring on the second image that is in the one-to-one correspondence with the landmark, to obtain M second Gaussian blurred images, where the M second Gaussian blurred images are the pose encoded images of the face rotation image, and M is a positive integer greater than 1, [0230], “. To be specific, for each first training image, the first training image is first detected by using the landmark detection algorithm, to obtain the location coordinates respectively corresponding to the N facial landmarks (facial landmark) in the first training images, then the N one-hot codes that are in a one-to-one correspondence with the N landmarks are generated based on the location coordinates respectively corresponding to the N landmarks, and then Gaussian blurring is performed by using the point whose value is 1 in each one-hot code as the center, to obtain the N Gaussian blurred images. In this way, after the pose encoding is performed on each first training image, the average is calculated. A specific manner of calculating the average may be adding up pixel values at locations corresponding to all the Gaussian blurred images, and then calculating an average”, [0287], where each of the plurality of first training images includes a face, and a presented rotation angle of the face included in each of the plurality of first training images is the face rotation angle, where the pose encoding unit 702 is further configured to perform pose encoding on a target face image based on two or more landmarks in the target face image, to obtain pose encoded images of the target face image, [0306]) [second encoded image indicated by the multiple landmarks used as the center for the blur and pose encoding].
Regarding claim 22, Rao et al. disclose the method according to claim 1. Rao et al. further indicate a computer-readable storage medium having executable instructions stored thereon, wherein the instructions are configured to, when executed by a processor, cause the processor to implement the method according to claim 1 ([0119]).
Claim(s) 2, 11, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rao et al. (US 20210012093 A1) as applied to claim 1 above, further in view of Moorer (US 8385590 B1).
Regarding claim 2, Rao et al. disclose the method according to claim 1. Rao et al. indicate the first encoded information comprises a first encoding code of at least one bit wherein the adjusting a pixel value of at least one first predetermined region of a to- be-encoded image according to the first encoded information so as to obtain a first encoded image comprises: adjusting the pixel value of the at least one first predetermined region of the to-be-encoded image according to at least one first predetermined pixel value, so as to obtain the first encoded image (pose encoded images of the target face image, [0018], “In a possible implementation, the performing pose encoding on the face image based on two or more landmarks in the face image, to obtain pose encoded images of the face image includes: detecting the face image by using a landmark detection algorithm, to obtain location coordinates respectively corresponding to N landmarks of the face image, where N is an integer greater than 1; constructing N first images having a same size as the face image, where the N first images are in a one-to-one correspondence with the N landmarks; and performing, by using each of the N landmarks as a center, Gaussian blurring on the first image that is in the one-to-one correspondence with the landmark, to obtain N first Gaussian blurred images, where the N first Gaussian blurred images are the pose encoded images of the face image. In this possible implementation, the N landmarks of the face image are first determined, and then Gaussian blurring is performed, by using each landmark as a center, on the first image corresponding to the landmark. In the manner in which the image pose encoding is implemented by performing Gaussian blurring by using the landmark, descriptions of a face pose are more accurate and robust, so that a higher-quality face rotation image is obtained”, [0019]-[0023]) [blurring and rotation interpreted as adjusting pixel value] but do not explicitly disclose at least one first predetermined region of the first encoded image comprises the at least one first predetermined pixel value, and the at least one first predetermined pixel value corresponds to the at least one bit of the first encoding code respectively.
Moorer teaches the first encoded information comprises a first encoding code of at least one bit; and wherein the adjusting a pixel value of at least one first predetermined region of a to- be-encoded image according to the first encoded information so as to obtain a first encoded image comprises: adjusting the pixel value of the at least one first predetermined region of the to-be- encoded image according to at least one first predetermined pixel value, so as to obtain the first encoded image, wherein at least one first predetermined region of the first encoded image comprises the at least one first predetermined pixel value, and the at least one first predetermined pixel value corresponds to the at least one bit of the first encoding code respectively (“The analyzing (1415) can include the use of a matched filter, which can be reduced to a cross-correlation between the output image of the combining (1410) and the original watermark pattern itself. Note that the watermark pattern can be broken up into a sync mark portion and a payload portion as well. When the value of the correlation (positive or negative) exceeds a predetermined threshold level (positive or negative), the watermark has been detected. More sophisticated decision methods can be used as well. For instance, machine-learning can be used to train a Gaussian-mixture decision surface to allow decisions that are more complex than thresholding. Moreover, results from multiple sets of frames can be combined to form a multidimensional decision surface”, col. 7, line 58 – col. 8, line 5, Multiple watermarks can be generated (2110). In some implantations, these watermarks can be created based in part or in whole on the video data received. In some implementations, additional data can be encoded into these watermarks. For example, a bit stream can be encoded in changes in amplitude or frequency between different sinusoid watermarks. In some implementations, a number (comparable to a barcode) can be encoded into one or more watermarks. In other implementations, only a single bit of information need be encoded into a watermark, where the single bit simply indicates that the video at hand has been watermarked by a given system, col. 8, lines 40-55) [bit stream, sync mark interpreted as teaching bit of code].
Rao et al. and Moorer are in the same art of encoding (Rao et al., abstract; Moorer, col. 13, lines 15-30). The combination of Moorer with Rao et al. will enable a predetermined pixel value corresponds to the at least one bit. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the predetermined pixel value of Moorer with the invention of Rao et al. as this was known at the time of filing, the combination would have predictable results, and as Moorer indicates “Computationally efficient detection of watermarks can result in the implementation of watermark detection on electronic devices with limited computational ability or battery power, such as mobile computing and phone devices. The size of the watermark pattern can be relatively small, since it is one dimensional. Thus, systems employing these techniques can readily search for multiple different watermarks, which can be fully known and recorded before detection begins since storage of small watermarks can be accomplished with minimal memory requirements” (col. 3, lines 20-30) demonstrating a computational benefit to combining inventions.
Regarding claim 11, Rao et al. disclose the method according to claim 1. Rao et al. do not explicitly disclose the first to-be-encoded information comprises at least one of: a device identifier, a time displayed on the display apparatus or a geographical location.
Moorer teaches the first to-be-encoded information comprises at least one of: a device identifier, a time displayed on the display apparatus or a geographical location (“FIG. 2D is a block diagram showing an example video distribution system (2400) used to distribute a watermarked video (2405) that has associated metadata. In some implementations, the video (2405) can be requested. The video (2405) can have associated metadata stored in a video metadata database (2460). A watermark generator (2410) can create a barcode-type watermark (2420) using a barcode creator (2415). The barcode creator (2415) can associate the barcode-type watermark (2420) with an entry in the video metadata database (2460), and the metadata can be related to the video (2405)”, col. 10, lines 50-65).
Rao et al. and Moorer are in the same art of encoding (Rao et al., abstract; Moorer, col. 13, lines 15-30). The combination of Moorer with Rao et al. will enable using metadata. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the metadata of Moorer with the invention of Rao et al. as this was known at the time of filing, the combination would have predictable results, and as Moorer indicates “Computationally efficient detection of watermarks can result in the implementation of watermark detection on electronic devices with limited computational ability or battery power, such as mobile computing and phone devices. The size of the watermark pattern can be relatively small, since it is one dimensional. Thus, systems employing these techniques can readily search for multiple different watermarks, which can be fully known and recorded before detection begins since storage of small watermarks can be accomplished with minimal memory requirements” (col. 3, lines 20-30) demonstrating a computational benefit to combining inventions.
Regarding claim 13, Rao et al. disclose the method according to claim 12. Rao et al. do not explicitly disclose the obtaining second encoded information according to a pixel value of at least one second predetermined region of the second encoded image comprises: determining a second encoding code corresponding to each of the at least one second predetermined region of the second encoded image, according to at least one second predetermined pixel value, so as to obtain the second encoded information, wherein the at least one second predetermined pixel value corresponds to the at least one second predetermined region of the second encoded image respectively.
Moorer teaches the obtaining second encoded information according to a pixel value of at least one second predetermined region of the second encoded image comprises: determining a second encoding code corresponding to each of the at least one second predetermined region of the second encoded image, according to at least one second predetermined pixel value, so as to obtain the second encoded information, wherein the at least one second predetermined pixel value corresponds to the at least one second predetermined region of the second encoded image respectively (“The analyzing (1415) can include the use of a matched filter, which can be reduced to a cross-correlation between the output image of the combining (1410) and the original watermark pattern itself. Note that the watermark pattern can be broken up into a sync mark portion and a payload portion as well. When the value of the correlation (positive or negative) exceeds a predetermined threshold level (positive or negative), the watermark has been detected. More sophisticated decision methods can be used as well. For instance, machine-learning can be used to train a Gaussian-mixture decision surface to allow decisions that are more complex than thresholding. Moreover, results from multiple sets of frames can be combined to form a multidimensional decision surface”, col. 7, line 58 – col. 8, line 5, Multiple watermarks can be generated (2110). In some implantations, these watermarks can be created based in part or in whole on the video data received. In some implementations, additional data can be encoded into these watermarks. For example, a bit stream can be encoded in changes in amplitude or frequency between different sinusoid watermarks. In some implementations, a number (comparable to a barcode) can be encoded into one or more watermarks. In other implementations, only a single bit of information need be encoded into a watermark, where the single bit simply indicates that the video at hand has been watermarked by a given system, col. 8, lines 40-55) [multiple frames interpreted as second set of information].
Rao et al. and Moorer are in the same art of encoding (Rao et al., abstract; Moorer, col. 13, lines 15-30). The combination of Moorer with Rao et al. will enable a predetermined pixel value corresponds to the at least one bit. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the predetermined pixel value of Moorer with the invention of Rao et al. as this was known at the time of filing, the combination would have predictable results, and as Moorer indicates “Computationally efficient detection of watermarks can result in the implementation of watermark detection on electronic devices with limited computational ability or battery power, such as mobile computing and phone devices. The size of the watermark pattern can be relatively small, since it is one dimensional. Thus, systems employing these techniques can readily search for multiple different watermarks, which can be fully known and recorded before detection begins since storage of small watermarks can be accomplished with minimal memory requirements” (col. 3, lines 20-30) demonstrating a computational benefit to combining inventions.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rao et al. (US 20210012093 A1) as applied to claim 1 above, further in view of Hanham et al. (US 20180175885 A1).
Regarding claim 7, Rao et al. disclose the method according to claim 1. Rao et al. do not explicitly disclose the first to-be-encoded information comprises a multi-bit first to-be-encoded code; and wherein the encoding a first to-be-encoded information corresponding to a display apparatus so as to obtain a first encoded information comprises: dividing the multi-bit first to-be- encoded code into a plurality of first to-be-encoded groups; determining a first check code corresponding to each of the plurality of first to-be- encoded groups, so as to obtain a plurality of first check codes; and obtaining the first encoded information according to the multi-bit first to-be- encoded code and the plurality of first check codes.
Hanham et al. teach the first to-be-encoded information comprises a multi-bit first to-be-encoded code; and wherein the encoding a first to-be-encoded information corresponding to a display apparatus so as to obtain a first encoded information comprises: dividing the multi-bit first to-be- encoded code into a plurality of first to-be-encoded groups; determining a first check code corresponding to each of the plurality of first to-be- encoded groups, so as to obtain a plurality of first check codes; and obtaining the first encoded information according to the multi-bit first to-be- encoded code and the plurality of first check codes (A solid state storage device, comprising: a non-volatile memory configured to: store further encoded data groups comprising a plurality of first encoded data groups collectively further encoded by a soft-decision parity scheme using a Low Density Parity Check (LDPC) code, each first encoded data group of the plurality being encoded using a hard decision parity scheme; a non-volatile memory controller communicatively coupled to the non-volatile memory and configured to access the plurality of further encoded data groups; and an integrated decoder, configured to: decode the plurality of further encoded data groups to give first decoded data groups by hard-decision decoding the parity in each first encoded data group within the further encoded data groups; generate log likelihood ratio (LLR) information to be associated with each of the first decoded data groups; and iteratively further decode the first decoded data groups using the LLR information, claim 1
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Rao et al. and Hanham et al. are in the same art of encoding (Rao et al., abstract; Hanham et al., abstract). The combination of Hanham et al. with Rao et al. will enable using a check code. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the check code of Hanham et al. with the invention of Rao et al. as this was known at the time of filing, the combination would have predictable results, and as Hanham et al. indicate this will improve error checking ([0006]-[0011]) which will improve the accuracy of the combined inventions.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rao et al. (US 20210012093 A1) as applied to claim 1 above, further in view of Anan et al. (US 20130170695 A1).
Regarding claim 8, Rao et al. disclose the method according to claim 1. Rao et al. further indicate the fusing the target image with the first encoded image to obtain a fused image comprises; determining a product between a pixel value of each of at least one pixel comprised in the target image and a first predetermined coefficient, so as to obtain a first pixel information (pose encoded images of the face image, [0018], multiplication and addition, [0186]); determining a product between a pixel value of each of at least one pixel comprised in the first encoded image and a second predetermined coefficient, so as to obtain a second pixel information (pose encoded images of the target face image, [0018], “In a possible implementation, the performing pose encoding on the face image based on two or more landmarks in the face image, to obtain pose encoded images of the face image includes: detecting the face image by using a landmark detection algorithm, to obtain location coordinates respectively corresponding to N landmarks of the face image, where N is an integer greater than 1; constructing N first images having a same size as the face image, where the N first images are in a one-to-one correspondence with the N landmarks; and performing, by using each of the N landmarks as a center, Gaussian blurring on the first image that is in the one-to-one correspondence with the landmark, to obtain N first Gaussian blurred images, where the N first Gaussian blurred images are the pose encoded images of the face image. In this possible implementation, the N landmarks of the face image are first determined, and then Gaussian blurring is performed, by using each landmark as a center, on the first image corresponding to the landmark. In the manner in which the image pose encoding is implemented by performing Gaussian blurring by using the landmark, descriptions of a face pose are more accurate and robust, so that a higher-quality face rotation image is obtained”, [0019]-[0023], multiplication and addition, [0186]); obtaining a third pixel information according to the first pixel information and the second pixel information and obtaining the fused image according to the third pixel information (It should be noted that in the method according to the first aspect, the generating a to-be-input signal based on the face image, the pose encoded images of the face image, and the pose encoded images of the target face image may be specifically obtaining the to-be-input signal by fusing the face image, the pose encoded images of the face image, and the pose encoded images of the target face image in a feature fusion manner, [0018], multiplication and addition, [0186], “It should be noted that in the method 500, the generating a to-be-input signal based on the face image, the pose encoded images of the face image, and the pose encoded images of the target face image may be specifically obtaining the to-be-input signal by fusing the face image, the pose encoded images of the face image, and the pose encoded images of the target face image in a feature fusion manner”, [0270]).
To the extent the product aspect is not made clear another reference is added.
Anan et al. teach embedding a watermark via multiplication (“In each of the above embodiments, the value of each pixel in the watermark pattern may be a coefficient by which the pixel value is multiplied. In this case, the watermark pattern superimposing unit superimposes the watermark pattern by multiplying the value of each pixel contained in the region where the watermark pattern and the reference region overlap each other by the value of the corresponding pixel in the watermark pattern”, [0137]).
Rao et al. and Anan et al. are in the same art of encoding (Rao et al., abstract; Anan et al., [0017]). The combination of Anan et al. with Rao et al. will enable determining a product between a pixel value of each of at least one pixel comprised in the target image and a first predetermined coefficient, so as to obtain a first pixel information. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the multiplication of Anan et al. with the invention of Rao et al. as this was known at the time of filing, the combination would have predictable results, and as Anan et al. indicate “As a result, the digital watermark embedding apparatus can better suppress the occurrence of the "defocusing" phenomenon in the video data in which the digital watermark pattern is embedded” ([0110]) which will improve the accuracy of the combined inventions.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rao et al. (US 20210012093 A1) as applied to claim 1 above, further in view of Kim et al. (US 20090002481 A1).
Regarding claim 9, Rao et al. disclose the method according to claim 1. Rao et al. do not explicitly disclose a shape of the first predetermined region comprises at least one of: a rectangle, a square, a diamond, a triangle or a circle.
Kim et al. teach a shape of the first predetermined region comprises at least one of: a rectangle, a square, a diamond, a triangle or a circle (“Upon receiving the divided base view image and additional view image from the block divider 310, the combined image recorder 320 records a combined image, which is generated by combining the blocks of the base view image and additional view image, in a payload area of the stereoscopic image bitstream, and outputs the stereoscopic image bitstream to the header information recorder 330.”, [0052], “According to an exemplary embodiment of the present invention, when the block size information is determined to be one number, the number of pixels in a row and the number of pixels in a column of the block are the same. Accordingly, when the block size information is determined to be one number, the block is a square. According to another exemplary embodiment of the present invention, the numbers of rows and columns of pixels may be each determined, and thus the block may be a square or a rectangle”, [0058]-[0059]).
Rao et al. and Kim et al. are in the same art of encoding (Rao et al., abstract; Kim et al., [0018]). The combination of Kim et al. with Rao et al. will enable a shape of the first predetermined region comprises at least one of a rectangle. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the rectangle of Kim et al. with the invention of Rao et al. as this was known at the time of filing, the combination would have predictable results, and as one of a limited number of shapes, would have been obvious to try and a design choice, as Kim et al. indicates this is convenient ([0082]) and as blocks used for embedding are typically rectangular shaped.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rao et al. (US 20210012093 A1) as applied to claim 12 above, further in view of Zhang et al. (US 20240386527 A1).
Regarding claim 18, Rao et al. disclose the method according to claim 12. Rao et al. do not explicitly disclose the decoding the second encoded information to obtain second to-be-encoded information corresponding to a display apparatus comprises: obtaining a plurality of second check codes according to the second encoded information; determining a second to-be-encoded group corresponding to each of the plurality of second check codes, so as to obtain a plurality of second to-be-encoded groups; and obtaining the second to-be-encoded information according to the plurality of second to-be-encoded groups; wherein the processing a to-be-processed image to obtain a second encoded image comprises: obtaining a feature map of at least one scale according to the to-be-processed image; and obtaining the second encoded image according to the feature map of the at least one scale; wherein the at least one scale comprises J scales, where J is a positive integer; and wherein the obtaining the second encoded image according to the feature map of the at least one scale comprises; in a case of 1≤j<J, obtaining a fused feature map of a j.sup.th scale according to the feature map of the j.sup.th scale and an upsampled feature map of the j.sup.th scale, wherein the upsampled feature map of the j.sup.th scale is obtained according to the feature map of a (j+1).sup.th scale and an upsampled feature map of the (j+1).sup.th scale, and the feature map of the j.sup.th scale is obtained according to the feature map of a (j−1).sup.th scale, where j is an integer greater than or equal to 1 and less than or equal to J; and obtaining the second encoded image according to a fused feature map of a 1.sup.st scale.
Zhang et al. teach obtaining a plurality of second check codes according to the second encoded information; determining a second to-be-encoded group corresponding to each of the plurality of second check codes, so as to obtain a plurality of second to-be-encoded groups; and obtaining the second to-be-encoded information according to the plurality of second to-be-encoded groups; wherein the processing a to-be-processed image to obtain a second encoded image comprises: obtaining a feature map of at least one scale according to the to-be-processed image; and obtaining the second encoded image according to the feature map of the at least one scale; wherein the at least one scale comprises J scales, where J is a positive integer; and wherein the obtaining the second encoded image according to the feature map of the at least one scale comprises; in a case of 1≤j<J, obtaining a fused feature map of a j.sup.th scale according to the feature map of the j.sup.th scale and an upsampled feature map of the j.sup.th scale, wherein the upsampled feature map of the j.sup.th scale is obtained according to the feature map of a (j+1).sup.th scale and an upsampled feature map of the (j+1).sup.th scale, and the feature map of the j.sup.th scale is obtained according to the feature map of a (j−1).sup.th scale, where j is an integer greater than or equal to 1 and less than or equal to J; and obtaining the second encoded image according to a fused feature map of a 1.sup.st scale (“In order to achieve the above goal, according to one aspect of the invention, a method for establishing a three-dimensional ultrasound image denoising model is provided, including: adding a speckle noise to three-dimensional biological structure images of a same size and without speckle noise, and forming a training data set of images before and after adding the speckle noise; establishing a three-dimensional denoising network based on an encoding-decoding structure to blindly denoise a noisy three-dimensional input image and output a three-dimensional image without speckle noise; the encoding structure in the encoding-decoding structure is used to obtain N feature maps of the three-dimensional input image, downsample the N feature maps, and further extract feature maps of different scales; the decoding structure in the encoding-decoding structure is used to take the feature maps obtained by the encoding structure as an input and reconstruct the three-dimensional image without speckle noise through upsampling; the encoding-decoding structure is divided into a plurality of stages by the downsampling structure and the upsampling structure. A number of convolution layers in a stage of a larger-scale feature map is less than or equal to a number of convolution layers in a stage of a smaller-scale feature map; N is a positive integer”, [0006]-[0008], “as shown in FIG. 1, the encoding structure in the encoding and decoding structure is used to obtain N feature maps of the three-dimensional input image, downsample the N feature maps, and further extract feature maps of different scales; N is a positive integer, and the value thereof is equal to the number of input channels of the first downsampling, as shown in FIG. 1. In the present embodiment, N=16. Specifically, the encoding structure downsamples the N feature maps three times”, [0045], The number of stages is specifically 2n+1. Since the encoding-decoding structure has a certain symmetry, the number of downsampling and the number of upsampling are equal, and n represents the number of upsampling or downsampling: in the present embodiment, the encoding-decoding structure is specifically divided into seven stages by the downsampling/upsampling structure, [0047]).
Rao et al. and Zhang et al. are in the same art of encoding (Rao et al., abstract; Zhang et al., abstract). The combination of Zhang et al. with Rao et al. will obtaining the second encoded image according to the feature map of the at least one scale. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the feature maps of Zhang et al. with the invention of Rao et al. as this was known at the time of filing, the combination would have predictable results, and as Zhang et al. indicate, “In this way, the speckle noise in the three-dimensional ultrasound image is removed while fully retaining the detailed information of the image, ensuring the image quality after denoising; statistics of a number of parameters and a floating point calculation amount of a single three-dimensional convolution layer at each stage at the corresponding feature map scale and input and output channels thereof reveals that when the scale of the shallow feature map is larger, the calculation amount of the convolution layer is larger and the number of parameters is smaller; compared with the existing encoding-decoding structure in which the number of convolution layers at each stage is set to be the same, in the invention, the distribution of convolution layers in the three-dimensional denoising network is optimized, so that the number of convolution layers in the stage of the larger-scale feature map is less than or equal to the number of convolution layers in the stage of the smaller-scale feature map, so as to effectively reduce the number of convolution layers when the layer feature map scale is large, greatly reduce the calculation amount of the network, and improve the real-time performance of denoising” ([0010]) providing a time efficiency benefit to combining inventions.
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) 21 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rao et al. (US 20210012093 A1).
Regarding claim 21, Rao et al. disclose an electronic device, comprising: one or more processors; and a memory configured to store one or more programs, wherein the one or more programs are configured to, when executed by the one or more processors, cause the one or more processors to implement the method according to claim 1 ([0117]-[0122]).
“An electronic device, comprising: one or more processors; and a memory configured to store one or more programs, wherein the one or more programs are configured to, when executed by the one or more processors, cause the one or more processors to implement the method according to claim 1” is a product by process claim limitation where the product is the device with processors and memory and the process is the method steps by programs which are not actually laid out in claim 21. MPEP §2113 recites “Product-by-Process claims are not limited to the manipulations of the recited steps, only the structure implied by the steps”. Thus, the scope of the claim is the processor. The structure includes the information and samples manipulated by the steps.
“To be given patentable weight, the printed matter and associated product must be in a functional relationship. A functional relationship can be found where the printed matter performs some function with respect to the product to which it is associated”. MPEP §2111.05(I)(A). When a claimed “computer-readable medium merely serves as a support for information or data, no functional relationship exists. MPEP §2111.05(III). The storage medium storing the claimed bitstream in claim 18 merely services as a support for the storage of the bitstream and provides no functional relationship between the stored bitstream and storage medium. Therefore, the structure bitstream, which scope is implied by the method steps, is non-functional descriptive material and given no patentable weight. MPEP §2111.05(III). Thus, the claim scope is just a storage medium storing data and is anticipated by REFERENCE which recites a storage medium storing a bitstream (Paragraph XYZ).
Allowable Subject Matter
Claims 3-6, 10, 14-17 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 and upon correction of relevant 35 USC 112b issues of the claims upon which the allowed claims depend.
The following art is cited as relevant to the objected to as allowable claims:
US 20210159999 A1: In one implementation, the M bit/N bit encoding is 64B/66B encoding; the second code block includes a type bit, and the type bit is one of 0x00, 0x78, 0x4B, 0x87, 0x99, 0xAA, 0xB4, 0xCC, 0xD2, 0xE1, and 0xFF; the second-type code block further includes an indicator bit, and the indicator bit is used to indicate a quantity of service bits in the second-type code block; and the type bit and the indicator bit are different bits. The type bit and the indicator bit are different bits, and therefore an existing control code block can be well extended to obtain a required second-type code block. In the embodiments of this application, the ingress PE device receives a CBR service bitstream, and converts the service bitstream into a code block stream for transmission in the XE system. The egress PE device receives the code block stream, and converts the code block stream into the CBR service bitstream. In the embodiments of this application, two types of M bit/N bit code blocks are defined: a first-type code block and a second-type code block. The first-type code block includes M service bits, the second-type code block includes L service bits, and L is an integer less than M and not less than 0. All payload bits in the first-type code block are service bits, and only some bits of payload bits in the second-type code block are service bits. A quantity of service bits in the first-type code block is invariable, and quantities of service bits in all first-type code blocks are all M. A quantity of service bits in the second-type code block is variable, and quantities of service bits in different first-type code blocks may be different.
US 12632509 B2: 1. A system, comprising: a first memory configured to store one or more encoded block sets corresponding to one or more weight tensors, each weight tensor including a number of n-bit integer weights; a matrix multiply accelerator including a second memory; a processor, operatively coupled to the first memory and the second memory, the processor configured to: store an encoded block set from the first memory to the second memory, the encoded block including a data field and an index field, the data field including a number of encoded weights, the index field including an index associated with each of the plurality of n-bit integer weights, where: the index field includes a plurality of indices, each index indicating an encoding type of an associated weight of the plurality of n-bit integer weights, the encoding type selected from: a zero magnitude type, indicating a weight with value zero; a small magnitude type, indicating a weight with zero-valued most significant n/2 bits; and a large magnitude type, indicating a weight with zero-valued least significant n/2 bits; and the data field includes: the most significant n/2 bits of each weight of the large magnitude encoding type; and the least significant n/2 bits of each weight of the small magnitude encoding type; where the matrix multiply accelerator (MMA) is configured to: read, from the second memory, an encoding type of a weight from the index field of an encoded block; when the encoding type is large magnitude encoding type or the small magnitude encoding type: read n/2 bits from the data field of the encoded block; and reconstruct an n-bit integer weight from the n/2 bits; and when the encoding type is the zero magnitude encoding type: reconstruct an n-bit integer weight with value zero.
US 20010046307 A1: In the preferred embodiment, the final step before the creation of the watermarked image is inserting the value C.sub.r into the modified image X.sub.r. In the modified image block X.sub.r, at least one bit of the image block is set to a predetermined value. Typically, C.sub.r is only inserted in bits that have been modified. Although preferably, each bit that has been modified corresponds to an insertion bit of C.sub.r, in an alternative embodiment, a bit C.sub.r does not correspond to each modified bit of the image block and therefore a value of C.sub.r is not inserted into every modified bit. In the preferred embodiment, where the LSB's of the image block are modified to be set to zero, and C.sub.r is inserted into the LSB of X.sub.r, a value of C.sub.r is inserted into each modified bit. Referring to FIG. 2A and the flowchart shown in 2B shows a method of extracting a watermark from a digital image Y.sub.r, including the steps of: for each I.times.J block, modifying at least a predetermined bit of the watermarked image Y.sub.r to a predetermined value (step 252), wherein the modified watermarked image Y.sub.r is Y.sub.r; extracting at least a predetermined bit from the watermarked image (step 254); calculating a digest of the values using a cryptographic hash function (step 256); combining the hashed output with the image block E.sub.r.
Conclusion
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/MICHELLE ENTEZARI/Primary Examiner, Art Unit 2671
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