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 1 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. Claim 1 recites at lines 7-9 “upsampling a second derived feature, the second derived feature derived from the second feature derived from the second feature based on a layer included in a second type of path of the neural network--”. The above recital of the second derived feature derived from the second feature derived from the second feature, renders the claim indefinite. It is unclear as to how the second derived feature derived from the second feature derived from the second feature ?. Also it is unclear whether the second derived feature and from the second feature are same or different?. Amendments/clarification are required. Claims 2-13 depends from claim 1 are also rejected.
Claim 14 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. Claim 1 recites at lines 7-8 “upsampling a second derived feature, the second derived feature derived from the second feature based on a layer included in a second type of path of the neural network----”. The above recital of the second derived feature derived from the second feature, renders the claim indefinite. It is unclear as to how the second derived feature derived from the second feature?. Also it is unclear whether the second derived feature and from the second feature are same or different?. Amendments/clarification are required. Claims 15-20 depends from claim 14 are also rejected.
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, 4-6, 8-10, 12-15 and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over LI, Zhen-le et al., (CN115909465A) hereafter LI in view of Wang et al., (US11631238) hereafter Wang.
Regarding claim 1 as best understood by the examiner, LI discloses a feature extraction method performed by one or more processors, the method (pages 5-6,9 and figs 3-4 shows and discloses a feature extraction method performed by one or more processors) comprising:
applying a first feature, which is extracted from multi-channel input data, to a bottleneck-based block included in a first type of path in a neural network, wherein the bottleneck-based block includes a squeeze and excitation (SE) block, and obtaining a second feature having a parameter that is less than a corresponding parameter of the first feature (fig 4, page 9 shows applying a first feature, which is extracted from multi-channel input data (page 9, fig 4 first feature block input data with different input channel (H X W XC)), to a bottleneck-based block included in a first type of path in a neural network (see fig 4 arrows for the data flow path in the neural network), wherein the bottleneck-based block includes a squeeze and excitation (SE) block, and obtaining a second feature (page 9 and fig 4 shows and discloses bottleneck-based block includes a squeeze and excitation (SE) block and obtaining a second feature (i.e output of the SE block as seen in fig 4) having a parameter that is less than (second feature H/2 X W/2 XC having half or less parameter than the first feature H X W X C parameter)) a corresponding parameter of the first feature (H X W X C)
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meeting the claim limitations, examiner notes that the specifics of a first feature, second feature and parameter and corresponding parameter are not required by the current claim);
a second type of path of the neural network, the upsampling causing the second derived feature to correspond to a size of a first derived feature derived from the first feature; and obtaining an intermediate feature applied to a head for a task of the neural network, based on the upsampled second derived feature and the first derived feature.
Wang discloses upsampling a second derived feature, the second derived feature derived from the second feature derived from the second feature based on a layer included in a second type of path of the neural network, the upsampling causing the second derived feature to correspond to a size of a first derived feature derived from the first feature; and obtaining an intermediate feature applied to a head for a task of the neural network, based on the upsampled second derived feature and the first derived feature
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Fig 2, cols 5 lines 15 through col 6 lines 67 shows and discloses upsampling a second derived feature, the second derived feature derived from the second feature derived from the second feature based on a layer included in a second type of path (with output y_pred1) of the neural network (output of block 7 feeding into Multi—scale feature fusion module is the second derived feature and the feature is processed by CBL (path in the neural network) is upsampled (see upsampling box), the upsampling causing the second derived feature (output of block 7) to correspond to a size of a first derived feature derived from the first feature (the output of block 5 feeding in to Multi-scale feature fusion module meets the above limitations, also the col 5-6 discloses the convolution processing with a kernel and stride (i.e a size)); and obtaining an intermediate feature (fig 2 shows the output of the concat block (i.e upsampled second derived feature)) applied to a head for a task of the neural network (i.e y_pred2 head) for detection/classification (i.e task), based on the upsampled second derived feature and the first derived feature (fig 2 shows the output of the concat block (i.e upsampled second derived feature which gets the input from the block 7 feature path (i.e the first derived feature) see arrows of the processing in fig 2., examiner notes that the specifics of “intermediate feature, head, task are not required by the current claim). Before the effective filing date of the invention was made Wang and LI are combinable because they are from the same filed of endeavor and are analogous art of image processing. The suggestion/motivation would be efficient method/system which expands the receptive field while retaining more shallow features (col 4 lines 30-35). Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages of Wang in the method of LI to obtain the invention as specified in claim 1.
2. Regarding claim 2, LI and Wang disclose the feature extraction method of claim 1. LI disclose further wherein the bottleneck-based block comprises: a layer configured to perform a pointwise convolution operation (fig 3 shows the pointwise Conv layer meeting the claim limitations); a layer configured to perform a depthwise convolution operation (fig 3 shows the Depthwise Conv layer operation); and an SE block configured to perform a squeeze operation and an excitation operation (fig 4 shows the SE block (performing the Squeeze and Excitation) meeting the claim limitations).
3. Regarding claim 4, LI and Wang disclose the feature extraction method of claim 1. LI discloses further wherein the bottleneck-based block, in which a size of input data thereof is equal to a size of output data thereof, comprises an SE block configured to receive, as an input, an output of a layer configured to perform a depthwise convolution operation (
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fig 4 shows the output of the DW layer performing Depthwise convolution with a size H/2 X W/2 X C fed into the input of the SE block input size 1X1XC and the output of the SE block with a size 1X1XC (i.e equal size) meeting the above claim limitations).
4. Regarding claim 5, LI and Wang disclose the feature extraction method of claim 1. LI disclose further, wherein the bottleneck-based block, in which a size of input data thereof is different to a size of output data thereof, comprises a layer configured to perform a depthwise convolution operation of receiving an output of an SE block as an input (
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fig 4 the botteleneck block shows the input data size (i.e the bold grey arrow in the beginning) with a size W X H X C and the output data size (i.e the final output with bold arrow) with a size W’/2 X H’/2 X C’ (i.e different size) meeting the above claim limitations).
5. Regarding claim 6, LI and Wang disclose the feature extraction method of claim 1. LI disclose further, wherein the bottleneck-based block, in which a size of input data thereof is equal to a size of output data thereof, comprises a skip connection between two layers of the bottleneck-based block
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Fig 4 shows the bottle neck layers without a direct connection (i.e skip connection between the first three convolution blocks and the last two convolution blocks) meeting the claim limitations).
6. Regarding claim 8, LI and Wang disclose the feature extraction method of claim 1. Wang disclose further, wherein the first derived feature is obtained by applying the first feature to the layer included in the second type of path (fig 2 shows wherein the first derived feature is obtained by applying the first feature to the layer included in the second type of path (y_pred1)).
7. Regarding claim 9, LI and Wang disclose the feature extraction method of claim 1. Wang disclose further, wherein the obtaining of the intermediate feature comprises concatenating the upsampled second derived feature with the first derived feature to form the intermediate feature (fig 2 shows wherein the obtaining of the intermediate feature comprises concatenating the upsampled second derived feature with the first derived feature to form the intermediate feature).
8. Regarding claim 10, LI and Wang disclose the feature extraction method of claim 1. Wang disclose further wherein the task is object detection (fig 2, col 3 lines 35-47, col 5 lines 52-57 discloses the task as the object detection/recognition (y_pred) meeting the claim limitations), and the method further comprises: converting the intermediate feature into a feature corresponding to the task of object detection; and based on the converted feature, outputting an object detection result corresponding to the multi-channel input data (fig 2 shows converting the intermediate feature into a feature corresponding to the task of object detection (y_pred2); and based on the converted feature, outputting an object detection result corresponding to the multi-channel input data (y_pred2)).
9. Regarding claim 12, LI and Wang disclose the feature extraction method of claim 1. Wang disclose further wherein the task comprises an object detection task or an object recognition task (fig 2, col 3 lines 35-47, col 5 lines 52-57 discloses the task as the object detection/recognition (y_pred) meeting the claim limitations).
10. Claim 13 is a corresponding non-transitory computer-readable storage medium storing instructions executing instruction by the one or more processors and cause the one or more processors to perform the method of claim 1. See the explanation of claim 1. LI discloses a memory storing instructions executed by the central processor on page 5.
11. Claim 14 is a corresponding apparatus claim of claim 1. See the corresponding explanation of claim 1. LI discloses an apparatus comprising: one or more processors on page 5.
12. Claim 15 is a corresponding apparatus claim of claim 2. See the explanation of claim 2.
13. Claim 17 is a corresponding apparatus claim of claim 10. See the corresponding explanation of claim 10.
14. Claim 18 is a corresponding apparatus claim of claim 4. See the corresponding explanation of claim 4.
15. Claim 19 is a corresponding apparatus claim of claim 5. See the corresponding explanation of claim 5.
Claims 11 is rejected under 35 U.S.C. 103 as being unpatentable over LI in view of Wang and in further view of YANG, JIAN-YU (CN116958686A) hereafter YANG.
16. Regarding claim 11, LI and Wang disclose the feature extraction method of claim 1. LI and wang both discloses the input data to be an image. LI and Wang however are silent and fail to discloses wherein the multi-channel input data comprises data sensed by a radar.
YANG discloses wherein the multi-channel input data comprises data sensed by a radar (fig 2 and page 4 lines 1-13 shows and disclose wherein the multi-channel input data comprises data sensed by a radar). Before the effective filing date of the invention was made, LI, Wang and YANG are combinable because they are from the same field of endeavor and are analogous art of image processing. The suggestion/motivation would be an accurate, real-time and high efficiency radar image target identification on page 3. Therefore, it would be obvious and within one of ordinary skill in the art to have recognized the advantages of YANG in the method of LI and Wang to obtain the invention as specified in claim 11.
Examiner's Note: Examiner has cited figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Examiner has also cited references in PTO892 but not relied on, which are relevant and pertinent to the applicant’s disclosure, and may also be reading (anticipatory/obvious) on the claims and claimed limitations. Applicant is advised to consider the references in preparing the response/amendments in-order to expedite the prosecution.
Conclusion
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/JAYESH PATEL/
Primary Examiner
Art Unit 2677
/JAYESH A PATEL/Primary Examiner, Art Unit 2677
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