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 .
Response to Amendment
A preliminary amendment filed October 25, 2024 has been entered and made of record. Claims 3-4, 6-9, 14-15, 20, 25, 27-28, and 31 have been amended; and claims 10-11, 13, 16, 18-19, 21-24, and 29-30 have been cancelled. By this amendment, claims 1-9, 12, 14-15, 17, 20, 25-28, and 31-32 are now pending for examination.
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.
Claims 1-2, 14-15, 17, 28, and 31-32 are rejected under 35 U.S.C. 103 as being unpatentable over Quan Do et al, (“Highly efficient and scalable framework for high-speed super-resolution microscopy”, IEEE, Volume 9, July 5, 2021, pages 97053-97067), (thereafter references Quan) in view of Agrawal et al, (“Multiple signal classification algorithm for super-resolution fluorescence microscopy”, nature communications, vol. 7, no. 1, December 9, 2016)
Regarding claim 1, Quan discloses a computer-implemented method of
processing image data to produce an output image, the method comprising:
receiving image data comprising nd paragraph, Input to the algorithm is a video of a fluorescently labeled sample acquired using a high-speed camera, [i.e., receiving image data comprising video images, captured at different times, of part or all of a sample region containing a sample, “implicit by acquiring video of a fluorescently labeled sample with high-speed camera”]);
selecting from the stack of images a plurality of windows, each window comprising a respective stack of spatially-coincident sections of the images, (see at least: Page 97053, right-hand-column, 2nd paragraph, the algorithm considers a sub-video per pixel by using a small neighborhood kernel around it and generates a local super-resolved sub-image, [which the small neighborhood kernel around pixels technically defines one or more windows, and such pixels implicitly generate the local resolved sub-image], [i.e., selecting from the stack of images a plurality of windows, “implicit by using small neighborhood kernel around pixels”, each window comprising a respective stack of spatially-coincident sections of the images, “such pixels implicitly generate the local resolved sub-image”]);
for each window:
- decomposing the window into eigenimages and corresponding singular values, (see at least: Page 97054, right-hand-column, section II, 3rd paragraph, an input stack image in 3D (x-y-t) is divided into several partitions. Each partition is scanned for every pixel, where one pixel corresponds to a 3D-cropped window in step 11. Singular values and singular vectors are calculated for that window in step 12, [i.e., decomposing the window into eigenimages, “implicit by dividing the stack image in 3D (x-y-t) into several partitions”, and corresponding singular values, “calculating singular values”]);
wherein at least one of the decomposing, calculating or determining steps is carried out for a first window of the plurality of windows at the same time as at least one of the decomposing, calculating or determining steps is carried out for a second window of the plurality of windows, (see at least: page 97056, right-hand-column, section B, Multi-threading is the ability of a CPU to execute multiple threads concurrently. In our implementation in Windows, we divide the input stack image into partitions. ….. Then, each partition is sent to the core to process simultaneously, [which means that each partition comprises many windows, and that the processing of windows belonging to different partitions is implicitly carried out simultaneously, based on using multiple threads], [i.e., at the decomposing step, “dividing step”, is implicitly carried out for a first window at the same time as the decomposing step of the second window, “the processing of windows belonging to different partitions is implicitly carried out simultaneously]);
Quan does not expressly disclose a stack of images; calculating, for each of a plurality of test points in the window, a respective first value as a first function of the eigenimages of the window; calculating, for each of the plurality of test points in the window, a respective second value as a second function of the eigenimages of the window; determining, from the first and second values, a respective indicator value for each test point, representative of a likelihood of a part of said sample being present at a location of the sample region corresponding to the test point; and combining the indicator values to produce an output image of the part or all of the sample region.
However, Agrawal discloses stack of images, (see at least: Fig. 1, “(a) mean image”);
calculating, for each of a plurality of test points in the window, a respective first value as a first function of the eigenimages of the window, (see at least: Page 2, right-hand-column, last paragraph, the projections of the PSF (an example is given in supplementary Fig. 5) at a given test point on the range and the null subspace are determined, which we denote as d.PR (r' test) and d.PN (r' test), respectively, [i.e., calculating, for each of a plurality of test points in the window, “determining for given test point in the window”, a respective first value as a first function of the eigenimages of the window, “d.PR (r' test)”]);
calculating, for each of the plurality of test points in the window, a respective second value as a second function of the eigenimages of the window, (see at least: Page 2, right-hand-column, last paragraph, the projections of the PSF (an example is given in supplementary Fig. 5) at a given test point on the range and the null subspace are determined, which we denote as d.PR (r' test) and d.PN (r' test), respectively, [i.e., calculating, for each of the plurality of test points in the window, “determining for given test point in the window”, a respective second value as a second function of the eigenimages of the window, “d.PN (r' test)”]);
determining, from the first and second values, a respective indicator value for each test point, representative of a likelihood of a part of said sample being present at a location of the sample region corresponding to the test point, (see at least: page 3, left-hand-column, 1st paragraph, this property of “d.PN (r' test)”]) is used to compute an indicator function as shown in equation (1), [i.e., determining, from the first and second values, (see equation (1): d.PR (r' test) and d.PN (r' test)”, a respective indicator value for each test point, “the indicator function”, representative of a likelihood of a part of said sample being present at a location of the sample region corresponding to the test point, “see equation (1)”]); and
combining the indicator values to produce an output image of the part or all of the sample region, (see at least: Page 3, left-hand-column, 2nd paragraph, stitching the results for sliding windows can be simplified as sum of the indicator functions of all the sliding windows covering a test point, [i.e., combining the indicator values, “sum of the indicator functions of all the sliding windows covering a test point”, to produce an output image of the part or all of the sample region, “implicit by stitching the results for sliding windows”]);
Quan and Agrawal are combinable because they are both in the field of fluorescence microscopy. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify Quan, to determine the projections of the PSF at a given test point, as though by Agrawal, in order to compute an indicator function based on the projections of the PSF, (Agrawal, page 3, left-hand-column, 1st paragraph)
Regarding claim 2, the combination of Quan and Agrawal as whole discloses the limitations of claim 1.
Quan further discloses decomposing the first window into eigenimages and corresponding singular values at the same time as decomposing the second window into eigenimages and corresponding singular values, (see at least: page 97056, right-hand-column, section B, Multi-threading is the ability of a CPU to execute multiple threads concurrently with different windows, for dividing the input stack image into partitions. ….. Then, each partition is sent to the core to process simultaneously).
Regarding claim 14, the combination of Quan and Agrawal as whole discloses the limitations of claim 1.
Agrawal further discloses wherein the sample comprises one or more moving parts, (see at least: Page 7, left-hand-column, first paragraph, “artifacts inside the rings” correspond to the one or more moving parts in the sample), where the respective indicator value for one or more test points represents a likelihood of a moving part of said sample being present at a location of the sample region corresponding to the test point, (see at least: page 2, right-hand-column, though, page 3, left-hand-column, 1st paragraph, statistically less likely patterns, which include noise patterns, are characterized by small singular values, where the property of “d.PN (r' test)”]) is used to compute an indicator function as shown in equation (1), which represents the likelihood of ring artifacts of the sample being present at a location of the sample region corresponding to the test point).
Regarding claim 15, the combination of Quan and Agrawal as whole discloses the limitations of claim 1.
Agrawal further discloses wherein the sample is a fluorescing sample, (see at least: Page 2, left-hand-column, 3rd paragraph, implicit by using fluorescence blinking to enhance spatial resolution), and the respective indicator value for one or more test points represents a likelihood of a fluorophore being present at a location of the sample region corresponding to the test point, (see at least: page 3, left-hand-column, 1st paragraph, this property of “d.PN (r' test)”]) is used to compute an indicator function as shown in equation (1), [i.e., determining, from the first and second values, (see equation (1): d.PR (r' test) and d.PN (r' test)”, a respective indicator value for each test point, “the indicator function”, which implicitly represents a likelihood of a fluorophore being present at a location of the sample region corresponding to the test point]).
Regarding claim 17, claim 17 recites substantially similar limitations as set forth in claim 1. As such, claim 17 is rejected for at least similar rational.
The Examiner further acknowledged the following additional limitation(s): “an image processing system …, comprising: an input interface for receiving image data ….”. However, Quan discloses the “image processing system …, comprising: an input interface for receiving image data ….”, (see at least: page 97063, right-hand-column, 1st paragraph, “system platform”). Further, page 97054, lest-hand-column, 1st paragraph, input interface is implicit by using graphical user interface (GUI), and a plurality of processors, (see at least: page 97063, section B, “Intel Xeon Gold 6130 processor, CPU 2.1 GHz, 32 cores, 2 sockets, 16 cores per socket …).
Regarding claim 28, the combination of Quan and Agrawal as whole discloses the limitations of claim 17.
Quan further discloses wherein each of the plurality of processors comprises a respective graphics processing unit (GPU) or GPU core, (see at least: page 97065, section E, “GPU”).
Regarding claim 31, claim 17 recites substantially similar limitations as set forth in claim 1. As such, claim 17 is rejected for at least similar rational.
The Examiner further acknowledged the following additional limitation(s): “An imaging system comprising: an imaging apparatus … , an input interface arranged to receive the image data from the imaging apparatus”.
However, Quan discloses the imaging system comprising: an imaging apparatus
… , an input interface arranged to receive the image data from the imaging apparatus ….”, (see at least: Abstract, “microscopy implicit the apparatus”. Further, page 97063, right-hand-column, 1st paragraph, “system platform”). Further, page 97054, lest-hand-column, 1st paragraph, input interface is implicit by using graphical user interface (GUI), and a plurality of processors, (see at least: page 97063, section B, “Intel Xeon Gold 6130 processor, CPU 2.1 GHz, 32 cores, 2 sockets, 16 cores per socket …).
Regarding claim 32, the combination of Quan and Agrawal as whole discloses the limitations of claim 31.
Quan further discloses wherein the imaging apparatus is a microscope, (see at least: Abstract, the (MUSICAL) is a statistical super-resolution technique for wide-field of fluorescence microscopy, “i.e., the apparatus is a microscope”).
Claims 3, and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Quan and Agrawal et al, as applied to claim 1 above; and further in view of Acuna et al, (“MusiJ: an lmageJ plugin for video nanoscopy, Vol. 11, No. 5, May 1, 2020, Biomedical optics express)
Regarding claim 3, the combination of Quan and Agrawal as whole discloses the limitations of claim 1.
Agrawal further discloses calculating the respective first or second values for the first window and calculating the first or second values for the second window, (see at least: Page 2, right-hand-column, last paragraph, “d.PR (r' test) and d.PN (r' test)”, and “sliding windows”)
The combination of Quan and Agrawal as whole does not expressly disclose that the respective first or second values for the first window and the first or second values for the second window, are calculated at the same time.
However, Acuna discloses that the respective first or second values for the first window and the first or second values for the second window, are calculated at the same time, (see at least: page 2550, equation (2), and page 2552, section 2.1, as the image contains many non-overlapping regions, it is possible to process them in different threads of execution simultaneously, before merging the results into a single final image, (“i.e., implicitly calculating the first or second values for different windows/regions simultaneously”).
Quan, Agrawal, and Acuna are combinable because they are all in the field of fluorescence microscopy. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Quan and Agrawal, to use the image containing many non-overlapping regions, as though by Acuna, in order to process them in different threads of execution simultaneously, (Acuna, page 2552, section 2.1).
Regarding claim 8, the combination of Quan and Agrawal as whole discloses the limitations of claim 1.
Quan further discloses wherein each image in the stack of images comprises a plurality of pixels, and the method comprises selecting a respective window centered on each of the pixels of the images in the stack of images, (see at least: Page 97053, right-hand-column, 2nd paragraph, the algorithm considers a sub-video per pixel by using a small neighborhood kernel around it and generates a local super-resolved sub-image, [which the small neighborhood kernel around pixels technically defines one or more windows], and on page 97053, section II, an input stack image in 3D (x-y-t) is divided …, where one pixel corresponds to a 3D-cropped window in step 11, [i.e., the defined windows are implicitly centered on each pixels of the images in the stack of images]).
Regarding claim 9, the combination of Quan and Agrawal as whole discloses the limitations of claim 1.
Agrawal further discloses wherein the image data comprises a time-series of images of the entire sample region, (see at least: Page 2, left-hand-column, 1st paragraph, “series of images is recorded over time”), and wherein the method further comprises generating a plurality of stacks of images from the image data, each stack of images being of a different respective part of the sample region, (see at least: page 5, right-hand-column, 2nd paragraph, “implicit by acquiring three image stacks for sample 2, each with different excitation powers”);
processing each of the stacks of images to produce a respective output image of each part of the sample region; and combining the plurality of output images to produce an output image of the entire sample region, (see at least: page 2, right-hand-column, 2nd paragraph, where the soft window is, then, scanned over the whole image and the individual MUSIC reconstructions are stitched together to form the MUSICAL image, [i.e., combining the plurality of output images to produce an output image of the entire sample region implicitly by stitching together the individual MUSIC reconstructions to form the MUSICAL image]).
Claims 4-5, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Quan and Agrawal et al, as applied to claim 1 above; and further in view of Agrawal et al, (“Multiple signal classification algorithm for super-resolution fluorescence microscopy, Supplementary Information", December 9, 2016), (thereafter reference Agrawal (2)).
Regarding claim 4, the combination of Quan and Agrawal as whole discloses the limitations of claim 1.
The combination of Quan and Agrawal as whole does not expressly disclose wherein calculating each first value includes applying a first weight function to eigenimages of the window, and calculating each second value includes applying a second weight function to eigenimages of the window
However, Agrawal (2) discloses wherein calculating each first value includes applying a first weight function to eigenimages of the window, and calculating each second value includes applying a second weight function to eigenimages of the window, (see at least: page 30, equations (21), (22), (23), where “d.PR (r' test) and d.PN (r' test)”, correspond to first and second values, respectively. Further, pages 33- 34, a scale factor scale(p) can be used to scale the indicator function of MUSICAL, …the weights thus calculated for SynEx4Bck are plotted in Supplementary Figure 16(f), [i.e., implicitly applying a first weight function to eigenimages of the window and a second weight function to eigenimages of the window based on calculated weights]).
Quan, Agrawal, and Agrawal (2) are combinable because they are all in the field of fluorescence microscopy. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Quan and Agrawal, to calculate the weights for SynEx4Bck, as though by Agrawal (2) , in order to compensate for camera noise, (Agrawal (2) , page 16).
Regarding claim 5, the combination of Quan, Agrawal, and Agrawal (2) as a whole discloses the limitations of claim 4.
Agrawal (2) further discloses determining the first and/or second weight function based on the eigenimages and corresponding singular values of a plurality of the windows, (see at least: page 29, where the indicator function shown in equation (19) is based on the eigenimages and corresponding singular values of a plurality of the windows. Further, pages 33- 34, a scale factor scale(p) can be used to scale the indicator function of MUSICAL, [i.e., the scaled or weighted indicator function of MUSICAL corresponds to the first and/or second weight function]).
Regarding claim 7, the combination of Quan and Agrawal as whole discloses the limitations of claim 1.
The combination of Quan and Agrawal as whole does not expressly disclose wherein calculating the first value for each test point comprises calculating a first contribution to the test point of the eigenimages of the window that have a corresponding singular value greater than a first threshold value, and calculating the second value for each test point comprises calculating a second contribution to the test point of the eigenimages of the window that have a corresponding singular value less than a second threshold value.
However, Agrawal (2) discloses wherein calculating the first value for each test point comprises calculating a first contribution to the test point of the eigenimages of the window that have a corresponding singular value greater than a first threshold value, and calculating the second value for each test point comprises calculating a second contribution to the test point of the eigenimages of the window that have a corresponding singular value less than a second threshold value, (see at least: page 30, equations (21), (22), where the singular values (epsilon. i) represent the corresponding singular value, greater than a first threshold value (epsilon. o), and the projections d.PR (r' test) and d.PN (r' test), represent the first contribution to the test point of the eigenimages, and second contribution to the test point of the eigenimages, respectively).
Quan, Agrawal, and Agrawal (2) are combinable because they are all in the field of fluorescence microscopy. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Quan and Agrawal, to calculate the projection distances, as though by Agrawal (2), in order to enable direct stitching of the MUSIC images of different sliding windows, (Agrawal (2) , Page 30, first paragraph)
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Quan and Agrawal, and Agrawal (2), as applied to claim 4 above; and further in view of Ullrich et al, (US-PGPUB 20170102860)
The combination of Quan, Agrawal, and Agrawal (2) as a whole discloses the limitations of claim 4.
The combination of Quan, Agrawal, and Agrawal (2) as a whole does not expressly disclose receiving an input from a user and determining the first and/or second weight function based at least partially on said input.
However, Ullrich discloses receiving an input from a user and determining the first and/or second weight function based at least partially on said input, (see at least: Abstract, Fig. 1, Par. 0023-0024, 0026-0027, a triangular user interface element is displayed that allows a user to assign importance weights to three interrelated decision optimization objectives using a single user input selection gesture; and on Par. 0072, software may be implemented …. for receiving and interpreting user input for weight factor values for a tri-variable weight function (e.g., process flow as in FIG. 1), [i.e., receiving an input from a user, “software may be implemented …. for receiving and interpreting user input”, and determining the first and/or second weight function based at least partially on said input, “implicitly determining the weight function based on user’s input weight factor values “]).
Quan, Agrawal, Agrawal (2), and Ullrich are combinable because they are all in the field of fluorescence microscopy. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Quan, Agrawal, and Agrawal (2), to use the graphical user interface (100), as though by Ullrich, in order to receive and interpreting user input for weight factor values for the weighting function, (Ullrich, Par. 0006, 0072)
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Quan and Agrawal et al, as applied to claim 1 above; and further in view of Sun et al, (US-PGPUB 20190355126)
The combination of Quan and Agrawal as whole discloses the limitations of the
claim 1.
The combination of Quan and Agrawal as whole does not expressly disclose padding one or more stacks of images with artificial image data.
However, Sun et al discloses padding one or more stacks of images with artificial image data, (see at least: Par. 0062, the image stack becomes a padded feature map through the CNN training model, [i.e., padding one or more stacks of images with artificial image data implicitly through the CNN training model]).
Quan, Agrawal, and Sun et al are combinable because they are all in the field of image processing. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Quan and Agrawal, to use the CNN training model, as though by Sun et al, in order to generate a padded feature map through the CNN training model, (Sun, Par. 0062)
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Quan and Agrawal et al, as applied to claim 17 above; and further in view of Agrawal et al, (“Multiple signal classification algorithm for super-resolution fluorescence microscopy, Supplementary Information", December 9, 2016), (thereafter reference Agrawal (2)); and further in view of Ullrich et al, (US-PGPUB 20170102860)
The combination of Quan and Agrawal as whole discloses the limitations of the claim 1
The combination of Quan and Agrawal as whole does not expressly disclose wherein calculating each first value includes applying a first weight function to eigenimages of the window, and calculating each second value includes applying a second weight function to eigenimages of the window, and the image processing system comprises a user interface for receiving an input from a user for determining the first and/or second weight function.
However, Agrawal (2) discloses wherein calculating each first value includes applying a first weight function to eigenimages of the window, and calculating each second value includes applying a second weight function to eigenimages of the window, (see at least: page 30, equations (21), (22), (23), where “d.PR (r' test) and d.PN (r' test)”, correspond to first and second values, respectively. Further, pages 33- 34, a scale factor scale(p) can be used to scale the indicator function of MUSICAL, …the weights thus calculated for SynEx4Bck are plotted in Supplementary Figure 16(f), [i.e., implicitly applying a first weight function to eigenimages of the window and a second weight function to eigenimages of the window based on calculated weights]).
Quan, Agrawal, and Agrawal (2) are combinable because they are all in the field of fluorescence microscopy. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Quan and Agrawal, to calculate the weights for SynEx4Bck, as though by Agrawal (2) , in order to compensate for camera noise, (Agrawal (2) , page 16).
The combination of Quan, Agrawal, and Agrawal (2) does not expressly disclose that the image processing system comprises a user interface for receiving an input from a user for determining the first and/or second weight function
Ullrich discloses that the image processing system comprises a user interface for receiving an input from a user for determining the first and/or second weight function, (see at least: Abstract, Fig. 1, Par. 0023-0024, 0026-0027, a triangular user interface element is displayed that allows a user to assign importance weights to three interrelated decision optimization objectives using a single user input selection gesture; and on Par. 0072, software may be implemented …. for receiving and interpreting user input for weight factor values for a tri-variable weight function (e.g., process flow as in FIG. 1), [i.e., a user interface for receiving an input from a user, “software may be implemented …. for receiving and interpreting user input”, for determining the first and/or second weight function, “implicitly determining the weight function based on user’s input weight factor values “]).
Quan, Agrawal, Agrawal (2), and Ullrich are combinable because they are all in the field of fluorescence microscopy. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Quan, Agrawal, and Agrawal (2), to use the graphical user interface (100), as though by Ullrich, in order to receive and interpreting user input for weight factor values for the weighting function, (Ullrich, Par. 0006, 0072)
Claims 25-27 are rejected under 35 U.S.C. 103 as being unpatentable over Quan and Agrawal et al, as applied to claim 17 above; and further in view of Park et al, (US-PGPUB 20170124723)
Regarding claim 25, the combination of Quan and Agrawal as whole discloses the limitations of claim 17.
Quan further discloses data storage module arranged to store the image data, (see at least: page 97061, left-hand-column, 1st paragraph, implicit by using memory, such as RAM), and a scheduler, wherein the scheduler is configured to define the plurality of windows, and allocating each window to a respective thread of plurality of threads, (see at least: Page 97053, right-hand-column, 2nd paragraph, the algorithm considers a sub-video per pixel by using a small neighborhood kernel around it and generates a local super-resolved sub-image, [i.e., defining plurality of windows by generating a local super-resolved sub-image”]. Further, on page 97056, section B, dividing the input stack image into partitions, … then each partition is sent to the core to process simultaneously, …. Using various multi-threading techniques, such as the parallel patterns library’s …., [i.e., the CPU executes multiple threads by allocating each partition, “including window”, to the core to process simultaneously]), wherein each processor is arranged to retrieve, from the data storage module, image data in accordance with the respective window or windows allocated to the processor by the scheduler, (page 97056, section B, the CPU implicitly retrieves the stack image from memory and then executes threads).
The combination of Quan and Agrawal as whole does not expressly disclose allocating each window to a respective processor of the plurality of processors
However, park discloses allocating each window to respective processor of the plurality of processors, (see at least: Par. 0140-0150, the image data allocator 640 may allocate the image data to two or more image data processing units 630 and 635, and each of the image data processing units 630 and 635 may process the image data allocated by the image data allocator 640, in parallel, such as each of the image data processing units 630 and 635 may simultaneously process different image data)
Quan, Agrawal, and park are combinable because they are all in the field of image processing. Therefore, it would have been obvious to a person of ordinary skill in the art, to modify the combination of Quan and Agrawal, to use the image data allocator 640, as though by park, in order to allocate the image data to plurality of image data processing units, to simultaneously process the different image data in parallel, (park, Par. 0147-0150)
Regarding claim 26, the combination of Quan, Agrawal, and park as whole discloses the limitations of claim 25.
park further discloses wherein the scheduler is connected to each of the plurality of processors via a respective first communication channel, (see at least: Figs. 6-7, Par. 0137-0150, the image data allocator 640 may allocate the image data to the two or more image data processing units 630 and 635; and on Par. 0144, each of the image data processing units 630 and 635 may process the image data allocated by the image data allocator 640. That is, the image data allocator 640 implicitly transmits the image data to the processors 630, 635, via first communication channels, (see Figs. 6-7, the first and second arrows from the image data allocator 640 to the processors 630, 635), [i.e., wherein the scheduler, “image data allocator 640”, is connected to each of the plurality of processors, “630, 635 in Fig. 7”]); and
each of the plurality of processors is connected to the data storage module via a respective second communication channel, (see at least: Fig. 7, image data processing units 630 and 635 are connected to the internal memory 720; and on Par. 0157, the internal memory 720 may receive and store the image data processed by the image data processing units 630 and 635. That is, the processed image data by processing units 630 and 635 is implicitly transmitted to the internal memory 720, via a second communication channels, implicitly different from the first communication channel, (see Fig. 7, the first and second arrows from the processors 630, 635 to the internal memory 720), [i.e., each of the plurality of processors, “630 and 635 in Fig. 7”, is connected to the data storage module, “720 in Fig. 7”, via a second communication channel, implicitly different from the first communication channel]).
The combination of Quan, Agrawal, and park as whole does not expressly disclose wherein the second communication channels support a higher maximum rate of data transfer than the first communication channels.
At the time of the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to have the second communication channels support a higher maximum rate of data transfer than the first communication channels. Applicant has not disclosed that having the second communication channels support a higher maximum rate of data transfer than the first communication channels, provides an advantage, be used for a particular purpose, or solves a stated problem. One of ordinary skill in the art, furthermore, would have expected Applicant’s invention to perform equally well with either the first communication channels transmitting image data from the image data allocator 640 to the processors 630, 635 shown in Figs. 6-7, and the second communication channels transmitting the processing image data from the processors 630, 635 to the internal memory 720, as though by park, and the claimed second communication channels supporting a higher maximum rate of data transfer than the first communication channels, because both systems perform the same function of promptly processing the image data, (park, Par. 0004).
Regarding claim 27, the combination of Quan, Agrawal, and park as whole discloses the limitations of claim 25.
Quan further discloses wherein the scheduler comprises a central processing unit or a core of a central processing unit, (see at least: page 97056, section B, the CPU executes multiple threads, “i.e., the scheduler implicitly comprises CPU”).
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMARA ABDI whose telephone number is (571)272-0273. The examiner can normally be reached 9:00am-5:30pm.
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/AMARA ABDI/Primary Examiner, Art Unit 2668 10/14/2026
