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 Objections
Claims 2-8, 10-14, 16-20 are objected to because of the following informalities:
When claim language is repeating a limitation, all instances “a” and “an” should be changed to “the” for antecedent clarity. For example, claim 2 “wherein the determining [[an]] the overlay parameter based on depth information of [[a]] the three-dimensional feature map to be processed includes:”
Appropriate correction is required.
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 5 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 5 recites the limitation "the overlay parameters". There is insufficient antecedent basis for this limitation in the claim as only “an overlay parameter” has been declared prior. For examination purposes, "a number of the overlay parameters" is interpreted as “a number of overlay parameters”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 9-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because they pertain to a computer readable storage medium with no redefinition in the specification to exclude transitory forms of signal transmission. The examiner suggests amending the claims to pertain to a non-transitory computer readable storage medium.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, at Step 1, the claim is directed to a method, which is a statutory category of invention (Process).
At Step 2A Prong 1, Examiner notes that the claims recites an abstract idea. The claim language has been reproduced below:
A feature extraction method for a three-dimensional feature map, including: determining an overlay parameter based on depth information of a three-dimensional feature map to be processed (mathematical relationship);
decomposing the three-dimensional feature map into a plurality of target two-dimensional feature maps based on the depth information and the overlay parameter (mathematical relationship);
performing two-dimensional convolution processing on each of the plurality of target two-dimensional feature maps to obtain a plurality of initial feature maps (mathematical calculation);
and determining a target feature map corresponding to the three-dimensional feature map based on the plurality of initial feature maps (mathematical relationship).
At Step 2A Prong 2 and Step 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception.
Regarding claim 2, it is directed to the mathematical concept of “determining a weight value in a corresponding three-dimensional convolution kernel for the three-dimensional convolution processing on the three-dimensional feature map; and determining the overlay parameter based on a first weight value in the three-dimensional convolution kernel corresponding to the depth dimension”.
Under Step 2A Prong 2 and Step 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception.
Regarding claim 3, it is directed to the mathematical concept of “decomposing the three-dimensional feature map in a depth direction, so that the three-dimensional feature map is decomposed into a first number of two-dimensional feature maps, wherein the first number is an integer greater than 1; and expanding a channel dimension in each of the first number of two-dimensional feature maps based on the overlay parameter to obtain the first number of target two-dimensional feature maps”.
Under Step 2A Prong 2 and Step 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception.
Regarding claim 4, it is directed to the mathematical concept of “performing a movement by a set stride in a direction of the depth information of the three-dimensional feature map based on the overlay parameter; and determining the first number of target two-dimensional feature maps based on a first number of two-dimensional map groups obtained through a first number of times of movements”.
Under Step 2A Prong 2 and Step 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception.
Regarding claim 5, it is directed to the mathematical concept of “obtaining, based on each of the first number of times of movements, a two-dimensional feature map group including a second number of two-dimensional feature maps, the second number indicating a number of the overlay parameters; and determining each of the two-dimensional feature map groups as a feature map on one channel in the target two-dimensional feature map, to obtain the first number of target two-dimensional feature maps with the second number of channels”.
Under Step 2A Prong 2 and Step 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception.
Regarding claim 6, it is directed to the mathematical concept of “determining a corresponding two-dimensional convolution kernel for the two-dimensional convolution processing based on a corresponding three-dimensional convolution kernel for the three-dimensional convolution processing on the three-dimensional feature map; and performing, based on the two-dimensional convolution kernel, two-dimensional convolution processing on each of the plurality of target two-dimensional feature maps to obtain the plurality of initial feature maps”.
Under Step 2A Prong 2 and Step 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception.
Regarding claim 7, it is directed to the mathematical concept of “merging a first value of the corresponding depth dimension in the three-dimensional convolution kernel and a second value of a corresponding channel dimension in the three-dimensional convolution kernel, to obtain a third value; and determining the third value as a value of a corresponding channel dimension in the two-dimensional convolution kernel to obtain the two-dimensional convolution kernel with a reduced number of dimensions”.
Under Step 2A Prong 2 and Step 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception.
Regarding claim 8, it is directed to the mathematical concept of “overlaying the plurality of initial feature maps in the depth direction; and obtaining the target feature map whose depth information corresponds to a value equal to the number of the initial feature maps”.
Under Step 2A Prong 2 and Step 2B, the claim does not recite any additional elements that integrate the abstract idea into a practical application, nor do they amount to significantly more than the judicial exception.
Regarding claims 9-14, the claims are directed to a computer readable storage medium that practices the method of claims 1-4, 6, 8, respectively. The analysis of claims 1-4, 6, 8 applies equally to claims 9-14.
Regarding claim 15, at Step 1, the claim is directed to an electronic device, which is a statutory category of invention (Machine).
At Step 2A Prong 1, Examiner notes that the claims recites an abstract. The claim language has been reproduced below:
An electronic device, including: a processor;
and a memory, configured to store a processor-executable instruction,
wherein the processor is configured to read the executable instruction from the memory, and execute the instruction to implement the feature extraction method for a three-dimensional feature map according to claim 1.
At Step 2A Prong 2, the additional elements are bolded above. The additional elements do not integrate the abstract ideas into a practical application because the computer elements, which are recited at a high level of generality, provide conventional computer functions that do not impose any meaningful limits on practicing the abstract ideas. See MPEP 2106.05(f). The limitations “processor” and “memory” are merely generic computer components that are no more than the equivalent of reciting “apply it” with the judicial exception. The limitation “store a processor-executable instruction” is merely an insignificant extra-solution activity of storing data. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application and the claim is directed to the judicial exception.
At Step 2B, the additional elements do not, alone or in combination, amount to significantly more than the recited judicial exception. As set forth in step 2A prong 2 analysis, the functions of “store a processor-executable instruction” is recognized by the courts as well-understood routine and conventional. See MPEP 2106.05(d)(II). Furthermore, the “processor” and “memory” are the equivalent of adding the words “apply it” to the judicial exception and are mere instructions to implement the abstract idea on a computer. Even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, which do not provide an inventive concept. The claim is not eligible.
Regarding claims 16-20, the claims are directed to an electronic device that practices the method of claims 2-4, 6, 8, respectively. The analysis of claims 2-4, 6, 8 applies equally to claims 16-20.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-3, 6, 8, 9-11, 13-14, 15-17, 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (Enhanced Efficiency 3D Convolution Based on Optimal FPGA Accelerator, hereinafter “Wang”).
As per claim 1, Wang teaches A feature extraction method for a three-dimensional feature map, including: determining an overlay parameter based on depth information of a three-dimensional feature map to be processed (Wang: Section II, paragraph 1; R corresponding to the depth to be processed);
decomposing the three-dimensional feature map into a plurality of target two-dimensional feature maps based on the depth information and the overlay parameter (Wang: Figs. 4-5; Section III, Section III.A);
performing two-dimensional convolution processing on each of the plurality of target two-dimensional feature maps to obtain a plurality of initial feature maps (Wang: Figs. 4-5; Section III, Section III.A);
and determining a target feature map corresponding to the three-dimensional feature map based on the plurality of initial feature maps (Wang: Figs. 4-5 outputs; Section III, Section III.A).
As per claim 2, Wang further teaches The method according to claim 1, wherein the determining an overlay parameter based on depth information of a three-dimensional feature map to be processed includes: determining a weight value in a corresponding three-dimensional convolution kernel for the three-dimensional convolution processing on the three-dimensional feature map (Wang: Section II, paragraph 1; length R corresponding to the depth to be processed; Fig. 5, kernel values are based on the length);
and determining the overlay parameter based on a first weight value in the three-dimensional convolution kernel corresponding to the depth dimension (Wang: Section II, paragraph 1; length R corresponding to the depth; Fig. 5, kernel values are based on the length).
As per claim 3, Wang further teaches The method according to claim 1 wherein the decomposing the three-dimensional feature map into a plurality of target two-dimensional feature maps based on the depth information and the overlay parameter includes: decomposing the three-dimensional feature map in a depth direction, so that the three-dimensional feature map is decomposed into a first number of two-dimensional feature maps, wherein the first number is an integer greater than 1 (Wang: Fig. 4; Section III.A, the 3D input is decomposed into 3 2D feature maps);
and expanding a channel dimension in each of the first number of two-dimensional feature maps based on the overlay parameter to obtain the first number of target two-dimensional feature maps (Wang: Figs. 4-5; Section III, Section III.A, the 3D input map is divided into 3 channels for the 2D convolutions).
As per claim 6, Wang further teaches The method according to claim 1, wherein the performing two-dimensional convolution processing on each of the plurality of target two-dimensional feature maps to obtain a plurality of initial feature maps includes: determining a corresponding two-dimensional convolution kernel for the two-dimensional convolution processing based on a corresponding three-dimensional convolution kernel for the three-dimensional convolution processing on the three-dimensional feature map (Wang: Figs. 4-5; Section III, Section III.A, the 3D inputs are separated into 3 2D channels);
and performing, based on the two-dimensional convolution kernel, two-dimensional convolution processing on each of the plurality of target two-dimensional feature maps to obtain the plurality of initial feature maps (Wang: Figs. 4-5 outputs; Section III, Section III.A).
As per claim 8, Wang further teaches The method according to claim 1, wherein the determining a target feature map corresponding to the three-dimensional feature map based on the plurality of initial feature maps includes: overlaying the plurality of initial feature maps in the depth direction (Wang: Fig. 4; Section III.B);
and obtaining the target feature map whose depth information corresponds to a value equal to the number of the initial feature maps (Wang: Figs. 4-5; Sections II, III.B).
As per claims 9-11, 13-14, the claims are directed to a computer readable storage medium that implements the same or similar features as the method of claims 1-3, 6, 8, respectively, and are therefore rejected for at least the same reasons therein.
As per claims 15-17, 19-20, the claims are directed to an electronic device that implements the same or similar features as the method of claims 1-3, 6, 8, respectively, and are therefore rejected for at least the same reasons therein. Furthermore, Wang teaches a processor (Wang: Fig. 6 ZYNQ); and a memory (Wang: Fig. 6 external memory), configured to store a processor-executable instruction, wherein the processor is configured to read the executable instruction from the memory, and execute the instruction to implement the feature extraction method for a three-dimensional feature map (Wang: Section III.B).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 4-5, 12, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Ahmadi et al. (US 20220351036 A1, hereinafter “Ahmadi”).
As per claim 4, Wang teaches The method according to claim 3, wherein the expanding a channel dimension in each of the first number of two-dimensional feature maps based on the overlay parameter to obtain the first number of target two-dimensional feature includes:
However, while Wang discloses separating the inputs into groups of channels, Wang does not explicitly disclose the grouping being based on the movements. Thus, Wang does not teach performing a movement by a set stride in a direction of the depth information of the three-dimensional feature map based on the overlay parameter; and determining the first number of target two-dimensional feature maps based on a first number of two-dimensional map groups obtained through a first number of times of movements.
Ahmadi teaches performing a movement by a set stride in a direction of the depth information of the three-dimensional feature map based on the overlay parameter (Ahmadi: Fig. 5; [0088]);
and determining the first number of target two-dimensional feature maps based on a first number of two-dimensional map groups obtained through a first number of times of movements (Ahmadi: Fig. 8; [0097]).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to modify, with a reasonable expectation of success, the input partitioning of Wang with the grouping of Ahmadi. One would have been motivated to combine these references because both references disclose performing 3D convolutions with 2D convolutions, and combining prior art elements according to known methods to yield predictable results (the grouping of input data to perform convolution operations).
As per claim 5, Wang/Ahmadi further teaches The method according to claim 4, wherein the determining the first number of target two-dimensional feature maps based on a first number of two-dimensional map groups obtained through a first number of times of movements includes: obtaining, based on each of the first number of times of movements, a two-dimensional feature map group including a second number of two-dimensional feature maps, the second number indicating a number of the overlay parameters (Ahmadi: Fig. 15 element 1304 to 1406; [0135]);
and determining each of the two-dimensional feature map groups as a feature map on one channel in the target two-dimensional feature map, to obtain the first number of target two-dimensional feature maps with the second number of channels (Ahmadi: Fig. 15 element 1406 to 1502; [0135]).
As per claim 12, the claim is directed to a computer readable storage medium that implements the same or similar features as the method of claim 4, and is therefore rejected for at least the same reasons therein.
As per claim 18, the claim is directed to an electronic device that implements the same or similar features as the method of claim 4, and is therefore rejected for at least the same reasons therein.
Allowable Subject Matter
Claim 7 would be allowable if rewritten to overcome the rejections under 35 USC 101 set forth in this Office Action and rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
As to claim 7, the prior art of record does not teach or suggest a combination as claimed including: merging a first value of the corresponding depth dimension in the three-dimensional convolution kernel and a second value of a corresponding channel dimension in the three-dimensional convolution kernel, to obtain a third value; and determining the third value as a value of a corresponding channel dimension in the two-dimensional convolution kernel to obtain the two-dimensional convolution kernel with a reduced number of dimensions.
Wang discloses a number of 2D kernels corresponding the R, the 3D kernel length (Section III.A). Wang does not suggest determining the channel dimension by merging the length and channel dimensions. Therefore, Wang does not teach or suggest a combination as claimed including the limitations identified above.
Ahmadi discloses reshaping the weight tensor by splitting dimensions of the tensor (Fig. 16; [0136]). Ahmadi does not suggest determining the channel dimension by merging the length and channel dimensions. Therefore, Ahmadi does not teach or suggest a combination as claimed including the limitations identified above.
Tran et al. (A Closer Look at Spatiotemporal Convolutions for Action Recognition, hereinafter “Tran”) discloses performing a 3D convolution using “(2+1)D” convolution which comprises a 2D spatial convolution and 1D temporal convolution (Section 1). Tran does not suggest determining the channel dimension of the kernel based on two other dimensions. Therefore, Tran does not teach or suggest a combination as claimed including the limitations identified above.
Yang et al. (Reinventing 2D Convolutions for 3D Images, hereinafter “Yang”) discloses converting 2D convolutions into 3D convolutions by splitting kernels into three groups of different orientations (Section III.A). Yang does not suggest determining the channel dimension of the kernel based on two other dimensions. Therefore, Yang does not teach or suggest a combination as claimed including the limitations identified above.
Shacham et al. (US 20180005075 A1, hereinafter “Shacham”) discloses performing a #D convolution with 2D coefficient planes (Fig. 9a; [0080]). Shacham does not suggest determining the channel dimension of the kernel based on two other dimensions. Therefore, Shacham does not teach or suggest a combination as claimed including the limitations identified above.
Conclusion
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/P.N.L./
Phat LeExaminer, Art Unit 2182 (571) 272-0546
/ANDREW CALDWELL/Supervisory Patent Examiner, Art Unit 2182
