DETAILED ACTION
This correspondence is responsive to the application filed on March 12, 2024. Claims 1-10 are pending in the case, with claims 1 and 9-10 in independent form.
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 .
Summary of Detailed Action
Claims 1-10 are objected to regarding informalities.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 5, and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Lovell et al. in view of Tatsumi et al.
Claim Objections
Claims 1-10 are objected to because of the following informalities:
Change “[Claim 1] (Currently Amended)” to “1. (Currently Amended)”
Change “[Claim 2] (Currently Amended)” to “2. (Currently Amended)”
… repeat similar change pattern for all claims …
Change “[Claim 10] (Currently Amended)” to “10. (Currently Amended)”
Appropriate correction is required.
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 use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) recite(s) sufficient structure, materials, or acts to entirely perform the recited function. Such claim limitation(s) is/are: a step of acquiring structural data representing a structure of a neural network; a step of extracting a plurality of nodes for a matrix vector product from the structural data; a step of converting the extracted plurality of nodes into nodes in a convolutional layer; and a step of outputting the converted structural data in claim 10.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends 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 remove the structure, materials, or acts that performs the claimed function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient structure, materials, or acts to perform the claimed function.
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 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) subject matter at a general, high-level a data conversion, extract a plurality of nodes for a matrix vector product from the structural data; convert the extracted plurality of nodes into nodes in a convolutional layer; and output the converted structural data, which are mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I). Additionally, the recitation of extract a plurality of nodes for a matrix vector product from the structural data is also mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III). This judicial exception is not integrated into a practical application and the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 1-10 recite one of the four statutory categories of patent able subject matter and belong to the statutory class(es) of a process (method claim 9), a machine (system/apparatus claims 1-8), and an article of manufacture (non-transitory computer readable media claim 10).
Claim 1 recites an apparatus, thus a machine and one of the four statutory categories of patentable subject matter. However, claim 1 further recites a data conversion, extract a plurality of nodes for a matrix vector product from the structural data; convert the extracted plurality of nodes into nodes in a convolutional layer; and output the converted structural data, which are mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I). Additionally, the recitation of extract a plurality of nodes for a matrix vector product from the structural data is also mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
apparatus comprising: at least one memory storing instructions; and at least one processor configured to execute the instructions to (an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See also, MPEP 2106.05(f), MPEP 2106.04(d), 2019 Guidance, 84 FR 50 at 55, footnote 30.).
acquire structural data representing a structure of a neural network (An additional element of extra-solution activity that courts have identified is well understood, routine and conventional activity for receiving or transmitting data over a network, e.g., using the internet to gather data. See also, MPEP 2106.05(d)(II), MPEP 2106.05(g), 2019 Guidance, 84 FR 50 at 55, 2019 Guidance, 84 FR 50, footnote 31.).
Thus, the claim is directed to the abstract idea.
Further, the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself, because implementation on a computer (MPEP 2106.05(f)) cannot provide significantly more and transmitting data over a network is well-understood, routine and conventional (MPEP 2106.05(d) does not meaningfully limit the claims (MPEP 2106.04(d)) and the combination of additional elements does not provide an inventive concept. Thus, the claim is ineligible.
Claim 2, dependent on claim 1, recites additional abstract ideas to extract the nodes satisfying conditions that: the numbers of elements of inputs of the nodes are equal to each other; the numbers of elements of outputs of the nodes are equal to each other; parameters used in the nodes are the same as each other; and there is no dependency between the nodes, which are mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I). Additionally, the recitation of extract the nodes satisfying conditions is also mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
the processor is further configured to execute the instructions to (an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See also, MPEP 2106.05(f), MPEP 2106.04(d), 2019 Guidance, 84 FR 50 at 55, footnote 30.).
Claim 3, dependent on claim 2, recites additional abstract ideas to insert an adjustment node for adjusting a data format in front of and behind the converted node in the convolutional layer, which are mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I).
The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
the processor is further configured to execute the instructions to (an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See also, MPEP 2106.05(f), MPEP 2106.04(d), 2019 Guidance, 84 FR 50 at 55, footnote 30.).
Claim 4, dependent on claim 3, recites additional abstract ideas to insert, when the converted nodes in the convolutional layer are successively arranged in series, the adjustment node common to the plurality of nodes in the convolutional layers successively arranged in series, which are mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I).
The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
the processor is further configured to execute the instructions to (an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See also, MPEP 2106.05(f), MPEP 2106.04(d), 2019 Guidance, 84 FR 50 at 55, footnote 30.).
Claim 5, dependent on claim 1, recites additional abstract ideas to extract a node in a fully connected layer as a node for a matrix vector product, which are mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I). Additionally, the recitation of extract a node in a fully connected layer as a node for a matrix vector product is also mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
the processor is further configured to execute the instructions to (an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See also, MPEP 2106.05(f), MPEP 2106.04(d), 2019 Guidance, 84 FR 50 at 55, footnote 30.).
Claim 6, dependent on claim 1, recites additional abstract ideas to extract a node in an RNN (Recurrent Neural Network) layer as a node for a matrix vector product, decompose the extracted node in the RNN layer into a node of a first type and a node of a second type, the node of the first type being a node of a matrix vector product, and the node of the second type being a node other than the matrix vector product, and convert a plurality of nodes of the first type into nodes in the convolutional layer, which are mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I). Additionally, the recitations of extract a node in an RNN (Recurrent Neural Network) layer as a node for a matrix vector product, decompose the extracted node in the RNN layer into a node of a first type and a node of a second type, the node of the first type being a node of a matrix vector product, and the node of the second type being a node other than the matrix vector product, are also mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
the processor is further configured to execute the instructions to (an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See also, MPEP 2106.05(f), MPEP 2106.04(d), 2019 Guidance, 84 FR 50 at 55, footnote 30.).
Claim 7, dependent on claim 6, recites only additional abstract ideas for wherein the node of the first type is a node in a fully connected layer using an identity function as a non-linear activation function, and the node of the second type is a connecting node, a dividing node, or an element operation node, the connecting node being a node disposed in front of the node in the fully connected layer and connecting data, the dividing node being a node disposed behind the node in the fully connected layer and dividing the data, and the element operation node being a node disposed behind the dividing node and performing a predetermined operation, which are mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I).
Claim 8, dependent on claim 7, recites additional abstract ideas to express a process of the plurality of dividing nodes derived from nodes in different RNN layers as a process of one node, and express a process of the plurality of element operation nodes derived from the nodes in the different RNN layers as a process of one node, which are mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I). Additionally, the recitations of to express a process of the plurality of dividing nodes derived from nodes in different RNN layers as a process of one node, and express a process of the plurality of element operation nodes derived from the nodes in the different RNN layers as a process of one node, are also mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
the processor is further configured to execute the instructions to (an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See also, MPEP 2106.05(f), MPEP 2106.04(d), 2019 Guidance, 84 FR 50 at 55, footnote 30.).
Claim 9 recites a method, thus a process and one of the four statutory categories of patentable subject matter. However, claim 9 further recites for data conversion, extracting a plurality of nodes for a matrix vector product from the structural data; converting the extracted plurality of nodes into nodes in a convolutional layer; and outputting the converted structural data, which are mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I). Additionally, the recitation of extracting a plurality of nodes for a matrix vector product from the structural data is also mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
acquiring structural data representing a structure of a neural network (An additional element of extra-solution activity that courts have identified is well understood, routine and conventional activity for receiving or transmitting data over a network, e.g., using the internet to gather data. See also, MPEP 2106.05(d)(II), MPEP 2106.05(g), 2019 Guidance, 84 FR 50 at 55, 2019 Guidance, 84 FR 50, footnote 31.).
Thus, the claim is directed to the abstract idea.
Further, the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself, because transmitting data over a network is well-understood, routine and conventional (MPEP 2106.05(d) does not meaningfully limit the claims (MPEP 2106.04(d)) and the combination of additional elements does not provide an inventive concept. Thus, the claim is ineligible.
Claim 10 recites a non-transitory computer readable medium, thus an article of manufacture and one of the four statutory categories of patentable subject matter. However, claim 10 further recites a step of extracting a plurality of nodes for a matrix vector product from the structural data; a step of converting the extracted plurality of nodes into nodes in a convolutional layer; and a step of outputting the converted structural data, which are mathematical concepts including mathematical relationships, mathematical formulas or equations, and mathematical calculations. MPEP 210604(a)(2)(I). Additionally, the recitation of a step of extracting a plurality of nodes for a matrix vector product from the structural data is also mental processes or concepts that can be performed in the human mind, including observation, evaluation, judgment or opinion, or by a human using pen and paper. MPEP 210604(a)(2)(III).
The claim does not include any additional elements which integrate the abstract idea into a practical application since the additional elements consist of:
A non-transitory computer readable medium storing a program for causing a computer to perform (an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. See also, MPEP 2106.05(f), MPEP 2106.04(d), 2019 Guidance, 84 FR 50 at 55, footnote 30.).
a step of acquiring structural data representing a structure of a neural network (An additional element of extra-solution activity that courts have identified is well understood, routine and conventional activity for receiving or transmitting data over a network, e.g., using the internet to gather data. See also, MPEP 2106.05(d)(II), MPEP 2106.05(g), 2019 Guidance, 84 FR 50 at 55, 2019 Guidance, 84 FR 50, footnote 31.).
Thus, the claim is directed to the abstract idea.
Further, the additional elements, alone or in combination, do not provide significantly more than the abstract idea itself, because implementation on a computer (MPEP 2106.05(f)) cannot provide significantly more and transmitting data over a network is well-understood, routine and conventional (MPEP 2106.05(d) does not meaningfully limit the claims (MPEP 2106.04(d)) and the combination of additional elements does not provide an inventive concept. Thus, the claim is ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 5, and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Lovell et al. (Pub. No. US 2022/0366225 A1, filed May 14, 2021) hereinafter Lovell in view of Tatsumi et al. (WO 2021/156941 A1, Structure Conversion Device, Structure Conversion Method, and Structure Conversion Program, English Translation, 2021-08-12) hereinafter Tasumi.
Regarding claim 1, Lovell teaches:
A data conversion apparatus comprising (i.e., the flattening circuit may, based on the received configuration information to convert the input data into a one-dimensional data format, e.g., as illustrated in FIG. 2A and FIG. 2B. Lovell, Abstract, Figs 1-6, para 46, 11-19, 39, 54-57):
at least one memory storing instructions; and at least one processor configured to execute the instructions to (i.e., Lovell, Abstract, Figs 6, 1-5, para 21, 25, 39, 53-56):
acquire structural data representing a structure of a neural network;
Lovell teaches that, In embodiments, configuration register 302 may be implemented as an on-board processor storage or a type of circuit, e.g., a dedicated physical register that may be dynamically allocated. Configuration register 302 may further be used to store instructions that identify operands having various bits and/or other data. Lovell, Fig 3, para 39, 42. [0042] Input data may comprise input size information, such as height and width information, which may be obtained from configuration register 302 (acquire ), e.g., along with image data. In embodiments, flattening circuit 306 may use the information to flatten the data. acquiring configuration data of a neural network. Lovell, Fig 3, 4, para 42, 39, 18. Thus, Lovell teaches acquiring configuration data and input structure information of the neural network. Lovell does not explicitly disclose acquire structural data representing a structure structure of the neural network.
However, Tatsumi teaches in the field related to converting the structure of a neural network. Tatsumi, page 1. Tatsumi, which is analogous to the claimed invention because Tatsumi is directed to converting the structure of a neural network, teaches that, The information acquisition unit 21 acquires structural information 31 (acquiring acquire structural data representing a structure of a neural network), performance information 32, and request information 33. Specifically, the structural information 31, the performance information 32, and the request information 33 set by the user or the like of the structural conversion device 10 are read out from the storage device 12. The structure information 31 is information necessary for determining the conversion part in the neural network. The structure information 31 is information indicating the structure of the neural network (acquire structural data representing a structure of a neural network). Specifically, the structural information 31 is for clarifying the contents of inference processing such as layer type, weight information, neurons, feature map, and filter size in each of a plurality of layers constituting the neural network. It is necessary information for. The type of layer is a fully connected layer, a convolutional layer, or the like. Tatsumi, pages 3, 4.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement the data conversion apparatus of Lovell using the acquiring structural data representing a structure of a neural network of Tatsumi, with a reasonable expectation of success, in order to improve the processing speed of the neural network, Tasumi, page 2.
This would have provided the advantages of acquiring neural network structural information for use in improving the processing speed of the neural network.
extract a plurality of nodes for a matrix vector product from the structural data;
As discussed above, Lovell in view of Tasumi teaches the structural data of the neural network. Lovell illustrates and discloses extracts a plurality of input nodes for matrix vector product in Figs 2A, 2B and corresponding description. As depicted in FIG. 2A, each byte of input data represents one channel 202-204, denoted as input channel 0 through input channel 2. It is understood that input data may comprise source data, such as image or audio data that may be read from memory, or output data obtained from a neural network layer that may precede an FCN layer and represents, for example, a (partial) input map or input matrix (extract (read) a plurality of nodes (nodes, layer neurons) for a matrix vector product (for a matrix-vector product para 30, 31) from the ). Lovell, Figs 2A-2B, 1, 3-6, para 27, 29-30, 34, 37, 41, 45, 48.
convert the extracted plurality of nodes into nodes in a convolutional layer; and output the converted structural data.
As discussed above, Lovell in view of Tasumi teaches the structural data of the neural network. Lovell teaches that, In embodiments, once input data is flattened in this manner, a conventional two-dimensional convolutional accelerator (not shown in FIG. 2A) may be employed to perform operations associated with an MLP, advantageously, without incurring any additional hardware cost. In embodiments, the one-dimensional output of the flattening circuit is provided the input to a two-dimensional convolution hardware (convert the extracted plurality of nodes into nodes in a convolutional layer) that computes a result in the same manner as if calculating an FCN, for example, to perform object detection in an image. In embodiments, the convolutional accelerator may apply the sets of weights 230, 240 to flattened input data 220 and accumulate the result, as shown, to obtain a convolution output (convert the extracted plurality of nodes into nodes in a convolutional layer, and output the converted structural data) 246, 248, e.g., by configuring one weight (e.g., 232) for each of the data inputs (e.g., 203) and performing integer or fixed-point multiply-and-accumulate operations (e.g., 242), in line with an FCN that convolves weights 230, 240 over the entirety of the input data 202-204, e.g., to obtain output pixel values 246, 248 for an image (convert the extracted plurality of nodes into nodes in a convolutional layer, and output the converted structural data)). Typical multiply-and-accumulate operations in a convolution involve scalar (dot product) operations, i.e., the summation of multiplication results that represent partial dot products that are obtained by element-wise multiplications of input data and weight data. Lovell, Figs 2A-2B, 1, 3-6, para 30, 27, 29, 41, 45, 48.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the present application to implement the data conversion apparatus of Lovell using the acquiring structural data representing a structure of a neural network of Tatsumi, with a reasonable expectation of success, in order to improve the processing speed of the neural network, Tasumi, page 2.
This would have provided the advantages of acquiring neural network structural information for use in improving the processing speed of the neural network.
Claim 5, which depends from claim 1 and recites:
the processor is further configured to execute the instructions to extract a node in a fully connected layer as a node for a matrix vector product.
Lovell in view of Tasumi teaches apparatus of claim 1, including the processor is configured to execute the instructions to extract a node. Lovell teaches that, 0027] As depicted in FIG. 2A, each byte of input data represents one channel 202-204, denoted as input channel 0 through input channel 2. It is understood that input data may comprise source data, such as image or audio data that may be read from memory, or output data obtained from a neural network layer that may precede an FCN layer and represents, for example, a (partial) input map or input matrix (extract (read) a node in a FCN (nodes, layer neurons in a FCN fully connected layer) for a matrix vector product (for a matrix-vector product para 30, 31) from the ). Lovell, 27, 29-31, 34, 37. In embodiments, once input data is flattened in this manner, a conventional two-dimensional convolutional accelerator (not shown in FIG. 2A) may be employed to perform operations associated with an MLP, advantageously, without incurring any additional hardware cost. In embodiments, the one-dimensional output of the flattening circuit is provided the input to a two-dimensional convolution hardware that computes a result in the same manner as if calculating an FCN, for example, to perform object detection in an image. In embodiments, the convolutional accelerator may apply the sets of weights 230, 240 to flattened input data 220 and accumulate the result, as shown, to obtain a convolution output 246, 248, e.g., by configuring one weight (e.g., 232) for each of the data inputs (e.g., 203) and performing integer or fixed-point multiply-and-accumulate operations (e.g., 242), in line with an FCN that convolves weights 230, 240 over the entirety of the input data 202-204, e.g., to obtain output pixel values 246, 248 for an image. Typical multiply-and-accumulate operations in a convolution involve scalar (dot product) operations, i.e., the summation of multiplication results that represent partial dot products that are obtained by element-wise multiplications of input data and weight data. Lovell, Figs 2A-2B, 1, 3-6, para 30, 27, 29-31, 34, 37.
Claim 9 recites a method that parallels the apparatus of claim 1. Therefore the analysis discussed above with respect to claim 1 also applies to claim 9. Accordingly, claim 9 is rejected based on substantially the same rationale as set forth above with respect to claim 1. More specifically regarding a data conversion method ( Lovell, Abstract, Figs 1-6, para 46, 11-19, 39, 54-57).
Claim 10 recites a non-transitory computer readable medium that parallels the apparatus of claim 1. Therefore the analysis discussed above with respect to claim 1 also applies to claim 10. Accordingly, claim 10 is rejected based on substantially the same rationale as set forth above with respect to claim 1. More specifically regarding A non-transitory computer readable medium storing a program for causing a computer to perform ( Lovell, Abstract, Figs 1-6, para 46, 11-19, 39, 54-57).
Allowable Subject Matter
Claims 2-4 and 6-8 would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if the rejections under 35 U.S.C. 101 as being directed to an abstract idea without significantly more are overcome and if the objections regarding informalities are overcome.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US-20190138891-A1, US-20220222318-A1, US-11868878-B1, US-20220342666-A1.
MARTIN, Chris, CN 110046700 A, Hardware Convolution Layer of the Deep Neural Network, English Translation, 2018-11-02.
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