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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/21/2024 has/have been considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a rotation determination unit, configured to”, “a convolution kernel adjustment unit, coupled to the rotation determination unit, configured to”, and “a convolution operation unit, coupled to the convolution kernel adjustment unit, configured to” in claim 1, “the rotation determination unit is configured to” in claim 2, “the convolution kernel adjustment unit is configured to” in claims 3 and 5, “a padding processing unit, configured to: in claim 4, and “mapping unit, configured to” in claim 6.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim(s) 7, 9, and 12 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pu et al (ICCV 2023), hereinafter Pu.
-Regarding claim 7, Pu discloses a convolution operation method, comprising (Abstract; FIGS. 1-5
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): determining a rotation state of input data (FIG. 3(c);
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Sec. 3.2., Page 6591, 1st paragraph) ; selectively adjusting an initial convolution kernel according to the rotation state, thereby obtaining an adjusted convolution kernel (FIG. 3 (a)-(c); Sec. 3.1. – Sec. 3.3.); and performing a convolution operation based on the adjusted convolution kernel and the input data to obtain a feature map (FIG. 3(a); Sec. 3.3.).
-Regarding claim 9, Pu discloses the method of claim 7. Pu further discloses wherein the convolution kernel adjustment unit is configured to rotate convolution weights in the initial convolution kernel with a rotation angle that is indicated by the rotation state, thereby obtaining the adjusted convolution kernel (FIG. 2; Page 6591, 2nd Col., 3rd paragraph).
-Regarding claim 12, Pu discloses the method of claim 7. Pu further discloses performing, according to the rotation information, a mapping processing on a plurality of operation results that are generated by performing the convolution operation on the adjusted convolution kernel and the input data (FIGS. 1-3; equations (1)-(4)).
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.
Claim(s) 1, 3, and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pu et al (ICCV 2023), hereinafter Pu in view of Zhu et al (2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp.1797-1802), hereinafter Zhu.
-Regarding claim 1, Pu discloses a convolution operation module, comprising (Abstract; FIGS. 1-5): a rotation determination unit, configured to determine a rotation state of input data (FIG. 3(c); Page 6591, Sec. 3.2., 1st paragraph) ; a convolution kernel adjustment unit, coupled to the rotation determination unit, configured to selectively adjust an initial convolution kernel according to the rotation state, thereby obtaining an adjusted convolution kernel (FIG. 3 (a)-(c); Sec. 3.1. – Sec. 3.3.); and a convolution operation unit, coupled to the convolution kernel adjustment unit, configured to perform a convolution operation based on the adjusted convolution kernel and the input data to obtain a feature map (FIG. 3(a); Sec. 3.3.).
Pu does not disclose a convolution operation circuit.
In the same field of endeavor, Zhu teaches hardware structure of the convolutional neural network based on FPGA hardware development platform (Zhu: Abstract; FIGS. 1-11). Zhu further teaches a FPGA-based convolutional operation structure to accelerate the convolutional operation (Zhu: FIGS. 1-4, 7-10).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Pu with the teaching of Zhu by using a convolution operation circuit in order to accelerate the convolutional operation.
-Regarding claim 3, Pu in view Zhu teaches the circuit of claim 1. The combination further teaches wherein the convolution kernel adjustment unit is configured to rotate convolution weights in the initial convolution kernel with a rotation angle that is indicated by the rotation state, thereby obtaining the adjusted convolution kernel (Pu: FIG. 2; Page 6591, 2nd Col., 3rd paragraph).
-Regarding claim 6, Pu in view Zhu teaches the circuit of claim 1. The combination further teaches comprising: a mapping unit, configured to perform, according to the rotation information, a mapping processing on a plurality of operation results that are generated by performing the convolution operation on the adjusted convolution kernel and the input data (Pu: FIGS. 1-3; equations (1)-(4)).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pu et al (ICCV 2023), hereinafter Pu in view of Zhu et al (2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp.1797-1802), hereinafter Zhu, and further in view of Kaheel et al (2012 IEEE International Conference on Multimedia and Expo Workshops), hereinafter Kaheel.
-Regarding claim 2, Pu in view of Zhu teaches the circuit of claim 1.
Pu in view of Zhu does not teach to determine the rotation state based on metadata of a captured image to which the input data corresponds, device rotation information of an image capture device which generates the captured image and/or a feature of specific elements in the captured image.
However, Kaheel is an analogous art pertinent to the problem to be solved in this application and teaches a method to employ the acceleration values to calculate the
rotation angles for the capturing device (Kaheel: Abstract; FIGSD. 1-8). Kaheel further teaches determine the rotation state based on metadata of a captured image to which the input data corresponds, device rotation information of an image capture device which generates the captured image and/or a feature of specific elements in the captured image (Kaheel: Sec. 3.).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Pu in view of Zhu with the teaching of Kaheel by using image metadata in order to provide fast way to calculate rotation angles.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pu et al (ICCV 2023), hereinafter Pu in view of Kaheel et al (2012 IEEE International Conference on Multimedia and Expo Workshops), hereinafter Kaheel.
-Regarding claim 8, Pu discloses the method of claim 1.
Pu does not disclose to determine the rotation state based on metadata of a captured image to which the input data corresponds, device rotation information of an image capture device which generates the captured image and/or a feature of specific elements in the captured image.
In the same field of endeavor, Kaheel teaches a method to employ the acceleration values to calculate the rotation angles for the capturing device (Kaheel: Abstract; FIGSD. 1-8). Kaheel further teaches determine the rotation state based on metadata of a captured image to which the input data corresponds, device rotation information of an image capture device which generates the captured image and/or a feature of specific elements in the captured image (Kaheel: Sec. 3.).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Pu with the teaching of Kaheel by using image metadata in order to provide fast way to calculate rotation angles.
Claim(s) 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pu et al (ICCV 2023), hereinafter Pu in view of Zhu et al (2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp.1797-1802), hereinafter Zhu, and further in view of Mohamed et al (US 20210248467 A1), hereinafter Mohamed.
-Regarding claim 4, Pu in view of Zhu teaches the circuit of claim 1.
Pu in view of Zhu does not teach to perform a padding processing on the input data according to the rotation state, thereby obtaining padded input data; wherein a position of padding data in the padded input data is associated with the rotation state.
However, Mohamed is an analogous art pertinent to the problem to be solved in this application and teaches a method for implementing efficient equivariant convolutional neural networks (Mohamed: Abstract; FIGS. 1-10). Mohamed further teaches to perform a padding processing on the input data according to the rotation state, thereby obtaining padded input data; wherein a position of padding data in the padded input data is associated with the rotation state (Mohamed: [0055], “In order to preserve 90 degree rotation equivariance, an odd-sized input may be used and the stride and padding of convolution layers may be adjusted to …”; [0107]; [0131], “rotating the first set of input data by a rotation angle to form a second set of input data … adapting a stride and padding of the strided convolution to maintain equivariance …”; FIGS. 2-3).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Pu in view of Zhu with the teaching of Mohamed by performing a padding processing on the input data according to the rotation state in order to preserve the rotation equivariance.
-Regarding claim 5, Pu in view of Zhu, and further in view of Mohamed teaches the circuit of claim 1. The modification further teaches wherein the convolution kernel adjustment unit is configured to perform the convolution operation based on the adjusted convolution kernel and the padded input data, thereby obtaining the feature map (Pu: FIGS. 1-3).
Claim(s) 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Pu et al (ICCV 2023), hereinafter Pu in view of Mohamed et al (US 20210248467 A1), hereinafter Mohamed.
-Regarding claim 10, Pu in view of Zhu teaches the method of claim 7.
Pu does not disclose to perform a padding processing on the input data according to the rotation state, thereby obtaining padded input data; wherein a position of padding data in the padded input data is associated with the rotation state.
In the same field of endeavor, Mohamed teaches a method for implementing efficient equivariant convolutional neural networks (Mohamed: Abstract; FIGS. 1-10). Mohamed further teaches to perform a padding processing on the input data according to the rotation state, thereby obtaining padded input data; wherein a position of padding data in the padded input data is associated with the rotation state (Mohamed: [0055], “In order to preserve 90 degree rotation equivariance, an odd-sized input may be used and the stride and padding of convolution layers may be adjusted to …”; [0107]; [0131], “rotating the first set of input data by a rotation angle to form a second set of input data … adapting a stride and padding of the strided convolution to maintain equivariance …”; FIGS. 2-3).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Pu with the teaching of Mohamed by performing a padding processing on the input data according to the rotation state in order to preserve the rotation equivariance.
-Regarding claim 11, Pu in view of Mohamed teaches the method of claim 10. The combination further teaches wherein the convolution kernel adjustment unit is configured to perform the convolution operation based on the adjusted convolution kernel and the padded input data, thereby obtaining the feature map (Pu: FIGS. 1-3).
Conclusion
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/XIAO LIU/Primary Examiner, Art Unit 2664
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