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 .
In response to Applicant’s claims filed on December 3, 2024 claims 1-20 are now pending for examination in the application.
Priority
Acknowledgment is made of a claim for priority as a continuation of PCT/JP2023/018871, filed 05/22/2023, which claims foreign priority to JP2022-094174, filed 06/10/2022, under 35 U.S.C. § 119(a)-(d) or (f), and is also acknowledged. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statements (IDS) filed on 12/03/24 has been considered by the Examiner and made of record in the application file.
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.
Claims 1-8 and 11- 18 contain limitations invoking 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph as detailed in the following:
Claim 1:
“a computational unit that derives...”
“an encoder that encodes...”
has been interpreted under 35 U.S.C. 112 (f), or pre-AIA 35 U.S.C. 112 sixth paragraph, because it uses a generic placeholder “encoder” coupled with functional languages without reciting sufficient structure to achieve the function and equivalents thereof. Furthermore, the generic placeholder is not preceded by a structural modifier.
Claim 6:
“a control unit that controls...”
has been interpreted under 35 U.S.C. 112 (f), or pre-AIA 35 U.S.C. 112 sixth paragraph, because it uses a generic placeholder “unit” coupled with functional languages without reciting sufficient structure to achieve the function and equivalents thereof. Furthermore, the generic placeholder is not preceded by a structural modifier.
Claim 7:
“a control unit that controls...”
has been interpreted under 35 U.S.C. 112 (f), or pre-AIA 35 U.S.C. 112 sixth paragraph, because it uses a generic placeholder “unit” coupled with functional languages without reciting sufficient structure to achieve the function and equivalents thereof. Furthermore, the generic placeholder is not preceded by a structural modifier.
Claim 8:
“a feature map generator that...”
has been interpreted under 35 U.S.C. 112 (f), or pre-AIA 35 U.S.C. 112 sixth paragraph, because it uses a generic placeholder “generator” coupled with functional languages without reciting sufficient structure to achieve the function and equivalents thereof. Furthermore, the generic placeholder is not preceded by a structural modifier.
Claim 11:
“an decoder that decodes...”
“a first computational unit that derives...”
has been interpreted under 35 U.S.C. 112 (f), or pre-AIA 35 U.S.C. 112 sixth paragraph, because it uses a generic placeholder “decoder” coupled with functional languages without reciting sufficient structure to achieve the function and equivalents thereof. Furthermore, the generic placeholder is not preceded by a structural modifier.
Claim 14:
“a control unit that controls...”
has been interpreted under 35 U.S.C. 112 (f), or pre-AIA 35 U.S.C. 112 sixth paragraph, because it uses a generic placeholder “unit” coupled with functional languages without reciting sufficient structure to achieve the function and equivalents thereof. Furthermore, the generic placeholder is not preceded by a structural modifier.
Claim 15:
“a control unit that controls...”
has been interpreted under 35 U.S.C. 112 (f), or pre-AIA 35 U.S.C. 112 sixth paragraph, because it uses a generic placeholder “unit” coupled with functional languages without reciting sufficient structure to achieve the function and equivalents thereof. Furthermore, the generic placeholder is not preceded by a structural modifier.
Claim 16:
“a second computational unit...”
“an encoder that generates...”
has been interpreted under 35 U.S.C. 112 (f), or pre-AIA 35 U.S.C. 112 sixth paragraph, because it uses a generic placeholder “encoder” coupled with functional languages without reciting sufficient structure to achieve the function and equivalents thereof. Furthermore, the generic placeholder is not preceded by a structural modifier.
Claim 18:
“a feature map generator that...”
has been interpreted under 35 U.S.C. 112 (f), or pre-AIA 35 U.S.C. 112 sixth paragraph, because it uses a generic placeholder “generator” coupled with functional languages without reciting sufficient structure to achieve the function and equivalents thereof. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claims 1-8 and 10-18 have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: NONE. The specification fails to show the corresponding structures of the components.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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.
Claims 1-9 and 11-19 are 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 pre-AIA the applicant regards as the invention.
Claims 1-8 and 11-18 invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Claims 1-8 and 11- 18 are interpreted under 35 U.S.C. 112(f) (see above). Therefore Claim(s) 1-8, 11-18 contain placeholders that require corresponding structure(s). It is unclear whether the recited structure, material, or acts in these claims are sufficient for performing the claimed function because the Specification is unclear about the corresponding structure(s). The figures do not provide indications of corresponding structure(s).
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Dependent claims 9 and 19 are also rejected for inheriting the deficiencies of the independent/dependent claims from which they depend on.
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.
Claim 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The eligibility analysis in support of these findings is provided below, in accordance with the 2019 Revised Patent Subject Matter Eligibility Guidance, hereinafter 2019 PEG.
Step 1. In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is noted that the system and methods of claims 1-20 are directed to one of the eligible categories of subject matter and therefore satisfy Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the Mental Process & Mathematical Concepts enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1, 10, 11, and 20 are directed towards the Mathematical Concepts and Mental Process Grouping of Abstract Ideas.
Independent claim 1 recites the following limitations directed towards a Mental Process & Mathematical Concepts:
asymptotic value of an activation function of the computational layer subject to processing (The limitation recites a mathematical concept; deriving); and
an encoder that encodes the difference (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to encode a difference)
by a quantization- based method where a midpoint of a quantization step size is not set as a quantization level for zero input (The limitation recites a mathematical concept; quantizing data).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 1:
a computational unit (i.e., as a generic processor performing a generic computer function)
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Independent claim 10 recites the following limitations directed towards a Mental Process & Mathematical Concepts:
deriving a difference between a feature map that is a processing result of a computational layer subject (The limitation recites a mathematical concept; deriving)
to processing of a neural network and an asymptotic value of an activation function of the computational layer subject to processing (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to processing a neural network); and
encoding the difference by a quantization-based method where a midpoint of a quantization step size is not set as a quantization level for zero input (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to encode a difference).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application.
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Independent claim(s) 11 and 20 recite(s) the following limitations directed towards a Mental Process & Mathematical Concepts:
a decoder that decodes encoded data (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to decode encoded data)
to generate a difference between a feature map that is a processing result of a computational layer subject (The limitation recites a mathematical concept; calculating a difference)
to processing of a neural network and an asymptotic value of an activation function of the computational layer subject to processing (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to process a neural network);
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 11 and 20:
a first computational unit
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application.
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claims 1, 10, 11, and 20 are rejected under 35 U.S.C. 101.
With respect to claim(s) 2 and 17:
the computational unit derives a difference between the feature map and an asymptotic lower bound of the activation function (The limitation recites a mathematical concept; deriving).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 3:
Step 2A Prong One Analysis:
the encoder quantizes the difference by a method where the quantization level for the zero input is set to zero (The limitation recites a mathematical concept; quantizing).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 4:
Step 2A Prong One Analysis:
the encoder quantizes the difference by a method where a value obtained as a result of rounding input is set as a quantization level for the input (The limitation recites a mathematical concept; quantizing).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 5:
Step 2A Prong One Analysis:
Step 2A Prong Two Analysis:
the computational unit
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 6:
Step 2A Prong One Analysis:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
a control unit (i.e., as a generic processor performing a generic computer function) that controls the asymptotic value applied to the computational unit for each computational layer of the neural network (recites insignificant extra solution activity that amounts to controlling data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 7:
Step 2A Prong One Analysis:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
a control unit (i.e., as a generic processor performing a generic computer function) that controls whether or not to cause the encoder to encode the difference for each computational layer of the neural network (recites insignificant extra solution activity that amounts to controlling data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 8 and 18:
Step 2A Prong One Analysis:
a feature map generator that executes computation of the computational layer subject to processing of the neural network to generate the feature map (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate a map).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 9:
Step 2A Prong One Analysis:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
a storage unit that stores encoded data of the difference generated by the encoder (recites insignificant extra solution activity that amounts to storing data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 10:
Step 2A Prong One Analysis:
deriving a difference between a feature map that is a processing result of a computational layer subject to processing of a neural network and an asymptotic value of an activation function of the computational layer subject to processing (The limitation recites a mathematical concept; deriving); and
encoding the difference by a quantization-based method where a midpoint of a quantization step size is not set as a quantization level for zero input (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to encode a difference).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because the claim as drafted recites insignificant extrasolution activity.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 12:
Step 2A Prong One Analysis:
Step 2A Prong Two Analysis:
the first computational unit
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 13:
Step 2A Prong One Analysis:
Step 2A Prong Two Analysis:
the first computational unit
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 14:
Step 2A Prong One Analysis:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
a control unit (i.e., as a generic processor performing a generic computer function) that controls the asymptotic value applied to the computational unit for each computational layer of the neural network (recites insignificant extra solution activity that amounts to controlling data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 15:
Step 2A Prong One Analysis:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
a control unit (i.e., as a generic processor performing a generic computer function) that controls whether or not to cause the decoder to decode the encoded data for each computational layer of the neural network (recites insignificant extra solution activity that amounts to controlling data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 16:
Step 2A Prong One Analysis:
an encoder that generates the encoded data by encoding the difference by a quantization-based method where a midpoint of a quantization step size not set as a quantization level for zero input (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to generate encoded data).
Step 2A Prong Two Analysis:
a second computational unit
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
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) 1, 3-4, 6-11, 14-16, 18-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by KIM (US Pub. No. 20220137866).
As to claim 1, KIM discloses an information processing device comprising:
a computational unit (See Fig. 1A) that derives a difference between a feature map that is a processing result of a computational layer subject to processing of a neural network and an asymptotic value of an activation function of the computational layer subject to processing (Paragraph 251 discloses each layer is processed in one data access request unit. If the data size such as the weight value, the feature map, the kernel, the activation map, and the like of the artificial neural network model is larger than the available capacity of the cache memory of the processor, the corresponding data access request may be divided into a plurality of data access requests and in this case, the artificial neural network data locality of the artificial neural network model may be reconstructed); and
an encoder (Paragraph 221 discloses artificial neural network model may be a model such as a fully convolutional network (FCN) having VGG, VGG16, DenseNET, and an encoder-decoder structure, a deep neural network (DNN) such as SegNet, DeconvNet, DeepLAB V3+, or U-net, or SqueezeNet, Alexnet, ResNet18, MobileNet-v2, GoogLeNet, Resnet-v2, Resnet50, Resnet101, and Inception-v3) that encodes the difference by a quantization- based method where a midpoint of a quantization step size is not set as a quantization level for zero input (Paragraph 157 discloses a quantization algorithm of the kernel, the feature map, or the like of the compiled artificial neural network model).
As to claim 3, KIM discloses the information processing device according to claim 2, wherein
the encoder quantizes the difference by a method where the quantization level for the zero input is set to zero (Paragraph 221 discloses artificial neural network model may be a model such as a fully convolutional network (FCN) having VGG, VGG16, DenseNET, and an encoder-decoder structure, a deep neural network (DNN) and Paragraph 951 discloses the compiler may compile an artificial neural network model with optimization algorithms (e.g., Quantization, Pruning, Retraining, Layer fusion, Model Compression, Transfer Learning, AI Based Model Optimization, and another Model Optimizations)).
As to claim 4, KIM discloses the information processing device according to claim 2, wherein
the encoder quantizes the difference by a method where a value obtained as a result of rounding input is set as a quantization level for the input (Paragraph 221 discloses artificial neural network model may be a model such as a fully convolutional network (FCN) having VGG, VGG16, DenseNET, and an encoder-decoder structure, a deep neural network (DNN) and Paragraph 951 discloses the compiler may compile an artificial neural network model with optimization algorithms (e.g., Quantization, Pruning, Retraining, Layer fusion, Model Compression, Transfer Learning, AI Based Model Optimization, and another Model Optimizations) and Paragraph 951 discloses the compiler may compile an artificial neural network model with optimization algorithms (e.g., Quantization, Pruning, Retraining, Layer fusion, Model Compression, Transfer Learning, AI Based Model Optimization, and another Model Optimizations)).
As to claim 6, KIM discloses the information processing device according to claim 1, further comprising:
a control unit that controls the asymptotic value applied to the computational unit for each computational layer of the neural network (Paragraph 221 discloses artificial neural network memory controller).
As to claim 7, KIM discloses the information processing device according to claim 1, further comprising:
a control unit that controls whether or not to cause the encoder to encode the difference for each computational layer of the neural network (Paragraph 221 discloses artificial neural network memory controller).
As to claim 8, KIM discloses the information processing device according to claim 1, further comprising:
a feature map generator that executes computation of the computational layer subject to processing of the neural network to generate the feature map (Paragraph 923 discloses due to the characteristics of the artificial neural network model, when the input feature map and the kernel are convolved, an output feature map is generated, and the corresponding output feature map becomes the input feature map of the next layer).
As to claim 9, KIM discloses the information processing device according to claim 1, further comprising:
a storage unit that stores encoded data of the difference generated by the encoder (Paragraph 462 discloses the artificial neural network memory controller may be configured to set the storage area of the memory).
As to claim 10, KIM discloses an information processing method comprising:
deriving a difference between a feature map that is a processing result of a computational layer subject to processing of a neural network and an asymptotic value of an activation function of the computational layer subject to processing (Paragraph 251 discloses each layer is processed in one data access request unit. If the data size such as the weight value, the feature map, the kernel, the activation map, and the like of the artificial neural network model is larger than the available capacity of the cache memory of the processor, the corresponding data access request may be divided into a plurality of data access requests and in this case, the artificial neural network data locality of the artificial neural network model may be reconstructed); and
encoding the difference by a quantization-based method where a midpoint of a quantization step size is not set as a quantization level for zero input (Paragraph 221 discloses artificial neural network model may be a model such as a fully convolutional network (FCN) having VGG, VGG16, DenseNET, and an encoder-decoder structure, a deep neural network (DNN) such as SegNet, DeconvNet, DeepLAB V3+, or U-net, or SqueezeNet, Alexnet, ResNet18, MobileNet-v2, GoogLeNet, Resnet-v2, Resnet50, Resnet101, and Inception-v3 and Paragraph 157 discloses a quantization algorithm of the kernel, the feature map, or the like of the compiled artificial neural network model).
As to claim 11, KIM discloses an information processing device comprising:
a decoder (Paragraph 221 discloses artificial neural network model may be a model such as a fully convolutional network (FCN) having VGG, VGG16, DenseNET, and an encoder-decoder structure, a deep neural network (DNN) such as SegNet, DeconvNet, DeepLAB V3+, or U-net, or SqueezeNet, Alexnet, ResNet18, MobileNet-v2, GoogLeNet, Resnet-v2, Resnet50, Resnet101, and Inception-v3) that decodes encoded data to generate a difference between a feature map that is a processing result of a computational layer subject to processing of a neural network and an asymptotic value of an activation function of the computational layer subject to processing (Paragraph 251 discloses each layer is processed in one data access request unit. If the data size such as the weight value, the feature map, the kernel, the activation map, and the like of the artificial neural network model is larger than the available capacity of the cache memory of the processor, the corresponding data access request may be divided into a plurality of data access requests and in this case, the artificial neural network data locality of the artificial neural network model may be reconstructed); and
a first computational unit (See Fig. 1A) that derives the feature map using the difference and the asymptotic value (Paragraph 129 discloses the feature map tiling technique is an artificial neural network technique which divides a convolution, and as a convolutional area is divided, the feature map is divided to be calculated).
With respect to claim 14, it is rejected on grounds corresponding to above rejected claim 6, because claim 14 is substantially equivalent to claim 6.
As to claim 15, KIM discloses the information processing device according to claim 11, further comprising:
a control unit that controls whether or not to cause the decoder to decode the encoded data for each computational layer of the neural network (Paragraph 221 discloses artificial neural network memory controller).
As to claim 16, KIM discloses the information processing device according to claim 11, further comprising:
a second computational unit that derives a difference between the feature map of the computational layer subject to processing and the asymptotic value (Paragraph 251 discloses each layer is processed in one data access request unit. If the data size such as the weight value, the feature map, the kernel, the activation map, and the like of the artificial neural network model is larger than the available capacity of the cache memory of the processor, the corresponding data access request may be divided into a plurality of data access requests and in this case, the artificial neural network data locality of the artificial neural network model may be reconstructed); and
an encoder that generates the encoded data by encoding the difference by a quantization-based method where a midpoint of a quantization step size not set as a quantization level for zero input ((Paragraph 221 discloses artificial neural network model may be a model such as a fully convolutional network (FCN) having VGG, VGG16, DenseNET, and an encoder-decoder structure and Paragraph 157 discloses a quantization algorithm of the kernel, the feature map, or the like of the compiled artificial neural network model).
With respect to claim 18, it is rejected on grounds corresponding to above rejected claim 8, because claim 18 is substantially equivalent to claim 8.
As to claim 19, KIM discloses the information processing device according to claim 11, further comprising:
a storage unit that stores the encoded data, wherein the decoder decodes the encoded data read from the storage unit (Paragraph 462 discloses the artificial neural network memory controller may be configured to set the storage area of the memory).
As to claim 20, KIM discloses an information processing method comprising:
decoding encoded data to generate a difference between a feature map that is a processing result of a computational layer subject to processing of a neural network and an asymptotic value of an activation function of the computational layer subject to processing (Paragraph 221 discloses artificial neural network model may be a model such as a fully convolutional network (FCN) having VGG, VGG16, DenseNET, and an encoder-decoder structure and Paragraph 251 discloses each layer is processed in one data access request unit. If the data size such as the weight value, the feature map, the kernel, the activation map, and the like of the artificial neural network model is larger than the available capacity of the cache memory of the processor, the corresponding data access request may be divided into a plurality of data access requests and in this case, the artificial neural network data locality of the artificial neural network model may be reconstructed); and
deriving the feature map using the difference and the asymptotic value (Paragraph 129 discloses the feature map tiling technique is an artificial neural network technique which divides a convolution, and as a convolutional area is divided, the feature map is divided to be calculated).
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) 2, 5, 12-13, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over KIM (US Pub. No. 20220137866) in view of DAI et al. (US Pub. No. 20210241112).
The KIM reference teaches all the limitations of claim 1. With respect to claim 2, KIM does not disclose the computational unit derives a difference between the feature map and an asymptotic lower bound of the activation function.
However, DAI et al. teaches the information processing device according to claim 1, wherein
the computational unit derives a difference between the feature map and an asymptotic lower bound of the activation function (Paragraphs 194-195 discloses to impel the neuron/feature map channel to further encode more information, its corresponding hyperparameter is tuned down to weaken its corresponding channel regularization term in the objective function. In actual operation, for example, the corresponding hyperparameter γ.sub.c.sup.l may be multiplied by a coefficient less than 1 (for example, 0.9). Further, in order to ensure the speed of tuning, a value range for each y.sub.c.sup.l may be set to, for example, the upper and lower bounds of 1×10.sup.−3 and 1×10.sup.−10, respectively).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify KIM with DAI et al. This would have facilitated training a deep neural network.
The KIM reference teaches all the limitations of claim 1. With respect to claim 5, KIM does not disclose the computational unit derives a difference between the feature map and an asymptotic upper bound of the activation function.
However, DAI et al. teaches the information processing device according to claim 1, wherein
the computational unit derives a difference between the feature map and an asymptotic upper bound of the activation function (Paragraphs 194-195 discloses to impel the neuron/feature map channel to further encode more information, its corresponding hyperparameter is tuned down to weaken its corresponding channel regularization term in the objective function. In actual operation, for example, the corresponding hyperparameter γ.sub.c.sup.l may be multiplied by a coefficient less than 1 (for example, 0.9). Further, in order to ensure the speed of tuning, a value range for each y.sub.c.sup.l may be set to, for example, the upper and lower bounds of 1×10.sup.−3 and 1×10.sup.−10, respectively).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify KIM with DAI et al. This would have facilitated training a deep neural network.
The KIM reference teaches all the limitations of claim 1. With respect to claim 12, KIM does not disclose the first computational unit derives the feature map by adding an asymptotic lower bound of the activation function to the difference.
However, DAI et al. teaches the information processing device according to claim 11, wherein
the first computational unit derives the feature map by adding an asymptotic lower bound of the activation function to the difference (Paragraphs 194-195 discloses to impel the neuron/feature map channel to further encode more information, its corresponding hyperparameter is tuned down to weaken its corresponding channel regularization term in the objective function. In actual operation, for example, the corresponding hyperparameter γ.sub.c.sup.l may be multiplied by a coefficient less than 1 (for example, 0.9). Further, in order to ensure the speed of tuning, a value range for each y.sub.c.sup.l may be set to, for example, the upper and lower bounds of 1×10.sup.−3 and 1×10.sup.−10, respectively).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify KIM with DAI et al. This would have facilitated training a deep neural network.
With respect to claim 13, it is rejected on grounds corresponding to above rejected claim 2, because claim 13 is substantially equivalent to claim 2.
With respect to claim 17, it is rejected on grounds corresponding to above rejected claim 2, because claim 17 is substantially equivalent to claim 2.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Pub. No. 20220174328 is directed to High-Fidelity Generative Image Compression: Paragraphs 56-57 discloses he latent representation of the training data item using the hyper-encoder neural network and in accordance with current values of the hyper-encoder network parameters to generate a latent representation of the conditional entropy model, i.e., a “hyper-prior”. In one example, the hyper-encoder neural network is a convolutional neural network and the hyper-prior is a multi-channel feature map output by the final layer of the hyper-encoder neural network. The system quantizes and entropy encodes the hyper-prior. For example, the system can quantize the hyper-prior use a quantizing engine. Quantizing a value refers to mapping the value to a member of a discrete set of possible code symbols. For example, the set of possible code symbols may be integer values, and the system may perform quantization by rounding real-valued numbers to integer values. The system can entropy encode the quantized hyper-prior using, e.g., a pre-determined entropy model defined by one or more predetermined code symbol probability distributions. In one example, the predetermined entropy model may specify a respective predetermined code symbol probability distribution for each code symbol of the quantized hyper-prior. In this example, the system may entropy encode each code symbol of the quantized hyper-prior using the corresponding predetermined code symbol probability distribution. The system can use any appropriate entropy encoding technique, e.g., a Huffman encoding technique, or an arithmetic encoding technique.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NICHOLAS E ALLEN whose telephone number is (571)270-3562. The examiner can normally be reached Monday through Thursday 830-630.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Boris Gorney can be reached at (571) 270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/N.E.A/Examiner, Art Unit 2154
/BORIS GORNEY/Supervisory Patent Examiner, Art Unit 2154
Prosecution Insights