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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/24/2026 has been entered. Claims 1-20 are pending. Claims 1, 8 and 15 are currently amended.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding independent claims 1, 8 and 15
Step 1 – whether the claim falls within any statutory category. See MPEP 2106.03
Claim 1 is drawn to a method claim, claim 8 is drawn to a computer-readable storage medium claim and claim is drawn to an electronic equipment claim. Therefore, each of these claims falls under one of the four categories of statutory subject matter (process/method, machine/product/apparatus, manufacture, or composition of matter).
Step 2 Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding claim 1, the claim is directed to a neural network computation method comprising steps “determining a size of a first feature map obtained when perform a convolution computation on a current layer before performing a convolution computation on a next layer”, “dynamically adjusting the first feature map”, “determining a convolution computation order of the next layer according to the size of the first feature map and a size of a second feature map for a convolution supported by the next layer such that the convolution computation order of the next layer is that the first feature map is firstly performed convolution computation”, “performing convolution computation instructions for the next layer based on the convolution computation order” and “wherein the first feature map is a first feature map output by a last executed convolution subtask in the current layer, and the second feature map is a second feature map output by a firstly executed convolution subtask in the next layer”. Under the broadest reasonable interpretation and also based on Figure 1 of the Specification, the steps of “determining a size”, “dynamically adjust”, “determining a convolution computation order” and “performing convolution computation instruction” may be performed in the human mind using observation, evaluation, judgment, and opinion, and such mental observations, evaluations, judgment, and opinion fall within the “mental processes” grouping of abstract ideas.
Independent claim 8 is a computer-readable storage medium claim reciting similar limitations of claim 1 and is directed towards the abstract idea for similar reasons.
Independent claim 15 is an electronic equipment/apparatus claim reciting similar limitations of claim 1 and is directed towards the abstract idea for similar reasons.
Step 2A Prong 2 – whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Regarding independent claim 1, this claim recites additional elements of a “processor” for performing convolution computation”, “storing the first feature map into an on-chip cache unit” and “stored in a memory provisioning of on-chip cache unit”. The “memory provision of an on-chip cache unit” and the “processor” are a computer recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. Similarly, the claim also recites a neural network, which is used to generally apply the abstract idea without limiting how the neural network functions or without any details about how convolution computation is accomplished in the neural network. These limitations amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.5H)), and thus fails to integrate the exception into a practical application.
Regarding independent claim 8, this claim is drawn to a computer-readable storage medium claim reciting similar limitations of claim 1 and is rejected under the same rationale. Claim 8 also recites additional elements of a computer device, a memory provisioning of an on-chip cache unit, and a processor to determine a size of a first feature map, determine a convolution computation order, and perform convolution computation instructions. Similarly, the claim also recites a neural network, which is used to generally apply the abstract idea without limiting how the neural network functions or without any details about how convolution computation is accomplished in the neural network. These limitations amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.5H)), and thus fails to integrate the exception into a practical application.
Regarding independent claim 15, this claim is drawn to an electronic equipment (apparatus) claim recited similar limitations of claim 1 and is rejected under the same rationale. Claim 15 recites additional elements of a “processor” for performing convolution computation” and a “memory provisioning of an on-chip cache unit” for storing feature map. The memory provisioning of an on-chip cache unit and the processor are a computer recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. Similarly, the claim also recites a neural network, which is used to generally apply the abstract idea without limiting how the neural network functions or without any details about how convolution computation is accomplished in the neural network. These limitations amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.5H)), and thus fails to integrate the exception into a practical application.
Regarding dependent claims 2-7, 9-14 and 16-20
Claims 2-7, 9-14 and 16-20 depend on claims 1, 8 and 15, respectively. Claims 2-7, 9-14 and 16-20 merely narrow the previously cited abstract idea limitations. For the reasons described above with respect to independent claims 1, 8 and 15, these judicial exceptions are not meaningfully integrated into a practical application, or significantly more than the abstract ideas. The claims disclose similar limitations described for the independent claims above and do not provide anything more than the mathematical relationships and mental processes that are practically capable of being performed in the human mind with the assistance of pen and paper. Therefore, claims 2-7, 9-14 and 16-20 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. § 101.
Step 1 – whether the claim falls within any statutory category. See MPEP 2106.03
Claims 2-7, 9-14 and 16-20 are drawn to method, computer-readable storage medium and apparatus claims, respectively. Therefore, each of these claims falls under one of the four categories of statutory subject matter (process/method, machine/product/apparatus, manufacture, or composition of matter).
Step 2A Prong 1 – whether the claim recites a judicial exception. See MPEP 2106.04, subsection II.
Regarding claim 2, this claim recites limitations of “when the size of the first feature map is bigger than the size of the second feature map, dividing the first feature map into a first subfeature map and a second subfeature map, based on the size of the first feature map, wherein a size of the first subfeature map is equal to the size of the second feature map” and “using the first subfeature map as the second feature map for a convolution computation type in the next layer”. These limitations may be performed by comparing, dividing, and making decision on selection of data based on the counting and dividing, which may be practically performed in the human mind using observation, evaluation and mathematical calculations. Thus, these limitations are directed towards the abstract idea of “mental processes” grouping of abstract ideas and “mathematical concepts” grouping of abstract ideas.
Regarding claim 3, this claim recites limitations of “when the size of the first feature map is smaller than the size of the second feature map”, and “determining the second feature map for a convolution computation type of the next layer based on the first feature map and the third feature map”. These limitations may be performed by comparing, selecting and making decision on selection of data, which may be practically performed in the human mind using observation and evaluation. Thus, these limitations are directed towards the abstract idea of “mental processes” grouping of abstract ideas.
Regarding claim 4, this claim recites limitations of “when the size of the first feature map is bigger than the size of the second feature map, reducing the size of the first feature map to the same size of the second feature map based on the size of the second feature map” and “dividing an original feature map of the current layer according to the size of the reduced first feature map.” These limitations may be performed by comparing, reducing, and dividing the size of data, which may be practically performed in the human mind using observation, evaluation and mathematical calculations. Thus, these limitations are directed towards the abstract idea of “mental processes” grouping of abstract ideas and “mathematical concepts” grouping of abstract ideas.
Regarding claim 5, this claim recites “when the size of the first feature map is smaller than the size of the second feature map, increasing the size of the first feature map to the same size of the second feature map based on the size of the second feature map” and “dividing an original feature map of the current layer according to the size of the increased first feature map.” These limitations may be performed by comparing, reducing, and dividing the size of data, which may be practically performed in the human mind using observation, evaluation and mathematical calculations. Thus, these limitations are directed towards the abstract idea of “mental processes” grouping of abstract ideas and “mathematical concepts” grouping of abstract ideas.
Regarding claim 6, this claim recites “wherein the size of the first feature map is equal to the size of the second feature map, using the first feature map as the second feature map for a convolution computation type of the next layer.” These limitations may be performed by comparing the sizes of data and selecting data based on the comparison result, which may be practically performed in the human mind using observation and evaluation.. Thus, these limitations are directed towards the abstract idea of “mental processes” grouping of abstract ideas.
Regarding claim 7, this claim recites “determining a convolution computation order of the second feature map that subsequently needs the convolution computation in the next layer based on the order number of the feature map.” The step of “determining” is performed based on in the human mind using observation and evaluation, and thus these limitations are directed towards the abstract idea of “mental processes” grouping of abstract ideas.
Regarding claims 9-14, these claims are computer-readable storage medium claims that contain similar limitations of claims 2-7. Therefore, Claims 9-14 are rejected under the same rationale.
Regarding claims 16-20, these claims are apparatus claims that contain similar limitations of claims 2-7. Therefore, claims 16-20 are rejected under the same rationale.
Step 2A Prong 2 - whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
Regarding claim 2, this claim recites additional elements of “storing the second subfeature map into an off-chip memory unit. Under the broadest reasonable interpretation and also based on Figure 10a of the Specification, the “storing” limitation is output data in which a human being can observe a graph shown in Figure 10a and store/output data based on the graph, and thus, fails to integrate the exception into a practical application.
Regarding claim 3, this claim recites additional elements of “loading a third feature map in a domain adjacent to the first feature map from an off-chip memory”. Under the broadest reasonable interpretation and also based on Figure 10a of the Specification, the “loading” limitation is output data in which a human being can observe a graph shown in Figure 10a and load/output data based on the graph, and thus, fails to integrate the exception into a practical application.
Regarding claims 4-5, these claims recite any additional elements of a “processor” for performing convolution computation”. The processor is a computer recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. Similarly, the claims also recites a neural network, which is used to generally apply the abstract idea without limiting how the neural network functions or without any details about how convolution computation is accomplished in the neural network. These limitations amount to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.5H)), and thus fails to integrate the exception into a practical application.
Regarding claim 6, this claim does not recite any additional elements, and thus fails to integrate the exception into a practical application.
Regarding claim 7, this claim recites additional elements of “acquiring an order number of the second feature map that needs the convolution computation among second feature maps in the next layer”. The step of “acquiring” is mere data gathering, and thus is insignificant extra-solution activity, and thus fails to integrate the exception into a practical application.
Regarding claims 9-14, these claims are computer-readable storage medium claims that contain similar limitations of claims 2-7. Therefore, Claims 9-14 are rejected under the same rationale.
Regarding claims 16-20, these claims are apparatus claims that contain similar limitations of claims 2-7. Therefore, claims 16-20 are rejected under the same rationale.
Response to Amendment
In the Remarks, Applicant argues in substance that
Applicant argues that claims 1, 8 and 15 achieve quantifiable technical effect via hardware-software co-design:
Adjusting next-layer convolution order based on real-time feature map size eliminates prior art resource idleness when data is read from and written into an off-chip memory, which is caused by the off-chip memory (DDR) with a large space but a slow speed, an on-chip cache with a very fast speed but a small capacity and a large space requirement of all feature maps and weight data, decreasing the interlayer consumption of a network.
Direct reuse of on-chip cached feature maps avoids off-chip memory access, improving the computation efficiency. (See page 11 of Remarks).
In reply to this argument, determining whether the claim as a whole includes an improvement to a computer or to a technological field requires evaluation of the specification and the claim to ensure that a technical explanation of the asserted improvement is present in the specification, and that the claim reflects the asserted improvement. In this case, a technical explanation of the asserted improvement is present in the specification, but the claim does not reflect the asserted improvement. Nothing recited in the claims 1, 8 and 15 reflects specific technical improvement as described in parts (a) and (b) above. Thus, the claim as a whole does not integrate the judicial exception into a practical application such that the claim is directed to the judicial exception.
Conclusion
Any inquiry concerning this communication should be directed to CHAU T NGUYEN at telephone number (571)272-4092. The examiner can normally be reached on M-F from 8am to 5pm (PT).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cesar Paula, can be reached at telephone number 5712724128. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHAU T NGUYEN/Primary Examiner, Art Unit 2145
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