DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the Application filed on 7/20/2023. Claims 1-20 are pending in the case. Claims 1, 12, and 20 are independent claims.
Drawings
New corrected drawings in compliance with 37 C.F.R. § 1.121(d) are required in this application because portions of . Applicant is advised to employ the services of a competent patent draftsperson outside the Office, as the U.S. Patent and Trademark Office no longer prepares new drawings. The corrected drawings are required in reply to the Office action to avoid abandonment of the application. The requirement for corrected drawings will not be held in abeyance.
INFORMATION ON HOW TO EFFECT DRAWING CHANGES
Replacement Drawing Sheets
Drawing changes must be made by presenting replacement sheets which incorporate the desired changes and which comply with 37 C.F.R. § 1.84. An explanation of the changes made must be presented either in the drawing amendments section, or remarks, section of the amendment paper. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 C.F.R. § 1.121(d). A replacement sheet must include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of the amended drawing(s) must not be labeled as “amended.” If the changes to the drawing figure(s) are not accepted by the examiner, applicant will be notified of any required corrective action in the next Office action. No further drawing submission will be required, unless applicant is notified.
Identifying indicia, if provided, should include the title of the invention, inventor’s name, and application number, or docket number (if any) if an application number has not been assigned to the application. If this information is provided, it must be placed on the front of each sheet and within the top margin.
Annotated Drawing Sheets
A marked-up copy of any amended drawing figure, including annotations indicating the changes made, may be submitted or required by the examiner. The annotated drawing sheet(s) must be clearly labeled as “Annotated Sheet” and must be presented in the amendment or remarks section that explains the change(s) to the drawings.
Timing of Corrections
Applicant is required to submit acceptable corrected drawings within the time period set in the Office action. See 37 C.F.R. § 1.85(a). Failure to take corrective action within the set period will result in ABANDONMENT of the application.
If corrected drawings are required in a Notice of Allowability (PTOL-37), the new drawings MUST be filed within the THREE MONTH shortened statutory period set for reply in the “Notice of Allowability.” Extensions of time may NOT be obtained under the provisions of 37 C.F.R. § 1.136 for filing the corrected drawings after the mailing of a Notice of Allowability.
Claim Rejections - 35 U.S.C. § 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 20 is rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. During examination, the claims must be interpreted as broadly as their terms reasonably allow. In re American Academy of Science Tech Center, 367 F.3d 1359, 1369, 70 U.S.P.Q.2d 1827, 1834 (Fed. Cir. 2004). Independent claim 20 recites a “storage medium,” which is not comprehensively defined by the specification. The broadest reasonable interpretation of a claim drawn to a computer readable medium covers forms of transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media. For example, paragraph 0031 recites “stored as signals and/or states in a storage device.” Although paragraph 0075 defines “memory” as non-transitory, it is not clear whether this definition extends to “storage medium.” Although paragraphs 0084 and 0100 attempt to distinguish by reciting “although signal and/or state components [] are to be associated logically, storage thereof, for example, may reside in one or more different places in a tangible, physical memory, in an embodiment,” this is a nonlimiting example and “tangible” is inadequate. Transitory propagating signals are non-statutory subject matter. In re Nuijten, 500 F.3d 1346, 1356-57, 84 U.S.P.Q.2d 1495, 1502 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter). See also Subject Matter Eligibility of Computer Readable Media, 1351 Off. Gaz. Pat. Office 212 (Feb. 23, 2010). Examiner suggests adding the word “non-transitory.”
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “computing an estimate of a latency in an execution of a candidate neural network architecture [], the estimate of the latency in the execution of the candidate neural network architecture to be based, at least in part, on:” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “a combination of estimated latencies of individual kernels” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “an overhead latency estimator to design features of the candidate neural network architecture to determine an overhead latency estimate” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “[a candidate neural network architecture] to be implemented on a computing platform, the computing platform comprising a computing device hosting a compiler” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “[kernels] defined in the computing platform and to be executed by the candidate neural network architecture” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “application of [an overhead latency estimator], wherein the overhead latency estimator comprises trainable parameters determined from measured latencies of execution of sample neural networks on the computing platform” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “application of [an overhead latency estimator], wherein the overhead latency estimator comprises trainable parameters determined from measured latencies of execution of sample neural networks on the computing platform” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “[a candidate neural network architecture] to be implemented on a computing platform, the computing platform comprising a computing device hosting a compiler” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “[kernels] defined in the computing platform and to be executed by the candidate neural network architecture” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “application of [an overhead latency estimator], wherein the overhead latency estimator comprises trainable parameters determined from measured latencies of execution of sample neural networks on the computing platform” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “application of [an overhead latency estimator], wherein the overhead latency estimator comprises trainable parameters determined from measured latencies of execution of sample neural networks on the computing platform” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 2:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “application of the overhead latency estimator comprises multiplying the estimated latencies by scalars” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “the scalars being determined based, at least in part, on the trainable parameters” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 3:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “the combination of estimated latencies of the individual kernels comprises a sum of individual estimated latencies associated with the individual kernels” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “application of the overhead latency estimator comprises adding a latency overhead term to the sum” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 4:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “a first neural network to compute an estimated latency based, at least in part, on an input tensor” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “a first neural network to compute an estimated latency based, at least in part, on an input tensor” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “a second neural network having parameters trained to map sample neural networks of multiple neural network search spaces to the input tensor” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “a second neural network having parameters trained to map sample neural networks of multiple neural network search spaces to the input tensor” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “a first neural network to compute an estimated latency based, at least in part, on an input tensor” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “a first neural network to compute an estimated latency based, at least in part, on an input tensor” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “a second neural network having parameters trained to map sample neural networks of multiple neural network search spaces to the input tensor” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “a second neural network having parameters trained to map sample neural networks of multiple neural network search spaces to the input tensor” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 5:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein parameters of the first and second neural networks are trained separately” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “wherein parameters of the first and second neural networks are trained separately” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein parameters of the first and second neural networks are trained separately” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “wherein parameters of the first and second neural networks are trained separately” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 6:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein the multiple neural network search spaces include neural networks of different depths” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “wherein the multiple neural network search spaces include neural networks of different depths” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the multiple neural network search spaces include neural networks of different depths” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “wherein the multiple neural network search spaces include neural networks of different depths” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 7:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the overhead latency estimate is determined further based, at least in part, on [] one or more parameters descriptive of a topology of the candidate neural network architecture” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “application of the overhead latency estimator” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “application of the overhead latency estimator” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “application of the overhead latency estimator” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “application of the overhead latency estimator” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “application of the overhead latency estimator” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “application of the overhead latency estimator” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 8:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the combination of estimated latencies comprises a sum of latencies of the individual kernels” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein the individual kernels are executed by the candidate neural network architecture in a series” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the individual kernels are executed by the candidate neural network architecture in a series” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 9:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein the sample neural networks and the candidate neural network architecture are selected from a neural network architecture search (NAS) space” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the sample neural networks and the candidate neural network architecture are selected from a neural network architecture search (NAS) space” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 10:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “[an estimated execution latency of the second sample neural network] computed using the first update of the trainable parameters” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “application of a first training epoch to determine a first update of the trainable parameters based, at least in part, on a measured execution latency of a first sample neural network and an estimated execution latency of the first sample neural network” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “application of a first training epoch to determine a first update of the trainable parameters based, at least in part, on a measured execution latency of a first sample neural network and an estimated execution latency of the first sample neural network” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “application of a second training epoch to determine a second update of the trainable parameters based, at least in part, on a measured execution latency of a second sample neural network and an estimated execution latency of the second sample neural network” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “application of a second training epoch to determine a second update of the trainable parameters based, at least in part, on a measured execution latency of a second sample neural network and an estimated execution latency of the second sample neural network” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “application of a first training epoch to determine a first update of the trainable parameters based, at least in part, on a measured execution latency of a first sample neural network and an estimated execution latency of the first sample neural network” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “application of a first training epoch to determine a first update of the trainable parameters based, at least in part, on a measured execution latency of a first sample neural network and an estimated execution latency of the first sample neural network” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “application of a second training epoch to determine a second update of the trainable parameters based, at least in part, on a measured execution latency of a second sample neural network and an estimated execution latency of the second sample neural network” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “application of a second training epoch to determine a second update of the trainable parameters based, at least in part, on a measured execution latency of a second sample neural network and an estimated execution latency of the second sample neural network” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 11:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a process.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “the loss function computed based, at least in part, on the measured execution latency of the first sample neural network and the estimated execution latency of the first sample neural network” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein the first update of the trainable parameters is determined based, at least in part, on a gradient of a loss function” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “wherein the first update of the trainable parameters is determined based, at least in part, on a gradient of a loss function” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the first update of the trainable parameters is determined based, at least in part, on a gradient of a loss function” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “wherein the first update of the trainable parameters is determined based, at least in part, on a gradient of a loss function” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 12:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “compute an estimate of a latency in an execution of a candidate neural network architecture [], the estimate of the latency in the execution of the candidate neural network architecture to be based, at least in part, on:” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “a combination of estimated latencies of individual kernels” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “an overhead latency estimator to design features of the candidate neural network architecture to determine an overhead latency estimate” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “one or more memory devices” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “one or more processors coupled to the one or more memory devices” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “[a candidate neural network architecture] to be implemented on a computing platform, the computing platform comprising a computing device hosting a compiler” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “[kernels] defined in the computing platform and to be executed by the candidate neural network architecture” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “application of [an overhead latency estimator], wherein the overhead latency estimator comprises trainable parameters determined from measured latencies of execution of sample neural networks on the computing platform” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “application of [an overhead latency estimator], wherein the overhead latency estimator comprises trainable parameters determined from measured latencies of execution of sample neural networks on the computing platform” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “one or more memory devices” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “one or more processors coupled to the one or more memory devices” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “[a candidate neural network architecture] to be implemented on a computing platform, the computing platform comprising a computing device hosting a compiler” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “[kernels] defined in the computing platform and to be executed by the candidate neural network architecture” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “application of [an overhead latency estimator], wherein the overhead latency estimator comprises trainable parameters determined from measured latencies of execution of sample neural networks on the computing platform” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “application of [an overhead latency estimator], wherein the overhead latency estimator comprises trainable parameters determined from measured latencies of execution of sample neural networks on the computing platform” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 13:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “application of the overhead latency estimator comprises multiplying the estimated latencies by scalars” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “the scalars being determined based, at least in part, on the trainable parameters” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 14:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “the combination of estimated latencies of the individual kernels comprises a sum of individual estimated latencies associated with the individual kernels” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “application of the overhead latency estimator comprises adding a latency overhead term to the sum” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
The analysis of the parent claim is incorporated.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
The analysis of the parent claim is incorporated.
As to claim 15:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “a first neural network to compute an estimated latency based, at least in part, on an input tensor” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “a first neural network to compute an estimated latency based, at least in part, on an input tensor” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “a second neural network having parameters trained to map sample neural networks of multiple neural network search spaces to the input tensor” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “a second neural network having parameters trained to map sample neural networks of multiple neural network search spaces to the input tensor” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “a first neural network to compute an estimated latency based, at least in part, on an input tensor” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “a first neural network to compute an estimated latency based, at least in part, on an input tensor” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “a second neural network having parameters trained to map sample neural networks of multiple neural network search spaces to the input tensor” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “a second neural network having parameters trained to map sample neural networks of multiple neural network search spaces to the input tensor” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 16:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein parameters of the first and second neural networks are trained separately” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “wherein parameters of the first and second neural networks are trained separately” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein parameters of the first and second neural networks are trained separately” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “wherein parameters of the first and second neural networks are trained separately” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 17:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
The analysis of the parent claim is incorporated.
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “wherein the multiple neural network search spaces include neural networks of different depths” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “wherein the multiple neural network search spaces include neural networks of different depths” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “wherein the multiple neural network search spaces include neural networks of different depths” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “wherein the multiple neural network search spaces include neural networks of different depths” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 18:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “wherein the overhead latency estimate is determined further based, at least in part, on [] one or more parameters descriptive of a topology of the candidate neural network architecture” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “application of the overhead latency estimator” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “application of the overhead latency estimator” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “application of the overhead latency estimator” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “application of the overhead latency estimator” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “application of the overhead latency estimator” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “application of the overhead latency estimator” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 19:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a machine.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “[an estimated execution latency of the second sample neural network] computed using the first update of the trainable parameters” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “application of a first training epoch to determine a first update of the trainable parameters based, at least in part, on a measured execution latency of a first sample neural network and an estimated execution latency of the first sample neural network” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “application of a first training epoch to determine a first update of the trainable parameters based, at least in part, on a measured execution latency of a first sample neural network and an estimated execution latency of the first sample neural network” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
No, the limitation “application of a second training epoch to determine a second update of the trainable parameters based, at least in part, on a measured execution latency of a second sample neural network and an estimated execution latency of the second sample neural network” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “application of a second training epoch to determine a second update of the trainable parameters based, at least in part, on a measured execution latency of a second sample neural network and an estimated execution latency of the second sample neural network” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “application of a first training epoch to determine a first update of the trainable parameters based, at least in part, on a measured execution latency of a first sample neural network and an estimated execution latency of the first sample neural network” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “application of a first training epoch to determine a first update of the trainable parameters based, at least in part, on a measured execution latency of a first sample neural network and an estimated execution latency of the first sample neural network” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
No, the limitation “application of a second training epoch to determine a second update of the trainable parameters based, at least in part, on a measured execution latency of a second sample neural network and an estimated execution latency of the second sample neural network” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “application of a second training epoch to determine a second update of the trainable parameters based, at least in part, on a measured execution latency of a second sample neural network and an estimated execution latency of the second sample neural network” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
As to claim 20:
Step 1 Analysis: Is the claim to a process, machine, manufacture or composition of matter? See MPEP § 2106.03.
Yes, the claim is to a manufacture.
Step 2A Prong One Analysis: Does the claim recite an abstract idea, law of nature, or natural phenomenon? See MPEP § 2106.04(II)(A)(1).
Yes, the limitation “compute an estimate of a latency in an execution of a candidate neural network architecture [], the estimate of the latency in the execution of the candidate neural network architecture to be based, at least in part, on:” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “a combination of estimated latencies of individual kernels” is the abstract idea of a mathematical calculation. See MPEP § 2106.04(a)(2)(I)(C).
Yes, the limitation “an overhead latency estimator to design features of the candidate neural network architecture to determine an overhead latency” is the abstract idea of a mental process that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper (including an observation, evaluation, judgment, opinion). See MPEP § 2106.04(a)(2)(III).
Step 2A Prong Two Analysis: Does the claim recite additional elements that integrate the judicial exception into a practical application? See MPEP § 2106.04(d).
No, the limitation “a storage medium comprising computer-readable instructions stored thereon, the computer-readable instructions to be executable by one or more processors of a computing device” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “[a candidate neural network architecture] to be implemented on a computing platform, the computing platform comprising a computing device hosting a compiler” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “[kernels] defined in the computing platform and to be executed by the candidate neural network architecture” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
No, the limitation “application of [an overhead latency estimator], wherein the overhead latency estimator comprises trainable parameters determined from measured latencies of execution of sample neural networks on the computing platform” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP §§ 2106.04(d), 2106.05(f)(1).
No, the limitation “application of [an overhead latency estimator], wherein the overhead latency estimator comprises trainable parameters determined from measured latencies of execution of sample neural networks on the computing platform” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to integrate the judicial exception into a practical application.
Step 2B Analysis: Does the claim recite additional elements that amount to significantly more than the judicial exception? See MPEP § 2106.05.
No, the limitation “a storage medium comprising computer-readable instructions stored thereon, the computer-readable instructions to be executable by one or more processors of a computing device” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “[a candidate neural network architecture] to be implemented on a computing platform, the computing platform comprising a computing device hosting a compiler” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “[kernels] defined in the computing platform and to be executed by the candidate neural network architecture” is an additional element that generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP § 2106.05(h).
No, the limitation “application of [an overhead latency estimator], wherein the overhead latency estimator comprises trainable parameters determined from measured latencies of execution of sample neural networks on the computing platform” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer. See MPEP § 2106.05(f)(1).
No, the limitation “application of [an overhead latency estimator], wherein the overhead latency estimator comprises trainable parameters determined from measured latencies of execution of sample neural networks on the computing platform” is an additional element that amounts to adding the words “apply it” (or an equivalent) with the judicial exception, or merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP § 2106.05(f)(2).
The additional elements, taken alone or in combination, fail to amount to significantly more than the judicial exception.
Claim Rejections - 35 U.S.C. § 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 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 C.F.R. § 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. § 102(b)(2)(C) for any potential 35 U.S.C. § 102(a)(2) prior art against the later invention.
Claims 1-3, 7-14, and 18-20 are rejected under 35 U.S.C. § 103 as being unpatentable over Huang et al. (“Interference-Aware Latency Prediction With Kernels For Deep Neural Network,” 9-11 December 2022, https://ieeexplore.ieee.org/document/10062171, hereinafter Huang) in view of Kim et al. (US 2023/0050247 A1, hereinafter Kim).
As to independent claim 1, Huang teaches a computer-implemented method comprising:
computing an estimate of a latency in an execution of a candidate neural network architecture to be implemented on a computing platform (“Then, the total latency
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of DNN inference can be formulated as:
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,” page 1234 column right section “1) Single DNN workload prediction:” lines 6-8), [] the estimate of the latency in the execution of the candidate neural network architecture to be based, at least in part, on:
a combination of estimated latencies of individual kernels defined in the computing platform and to be executed by the candidate neural network architecture (“the execution latency of each kernel can be denoted by t1, t2…tm,” page 1234 column right section “1) Single DNN workload prediction:” lines 5-6); and
application of an overhead latency estimator to design features of the candidate neural network architecture to determine an overhead latency estimate (“the scheduling latency of each kernel can be denoted by s1, s2…sm,” page 1234 column right section “1) Single DNN workload prediction:” lines 4-5), wherein the overhead latency estimator comprises trainable parameters determined from measured latencies of execution of sample neural networks on the computing platform (“In data preparation, we perform a large number of inference tasks for different models. Each model is repeated several times with varying amounts of GPU resources assigned (e.g., 10%, 25%, 50%, 90%) to obtain the values of
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, and α •
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for each type of kernel as sample points. Then we perform a least-squares fit for these parameters of each kernel separately, and eventually,
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of each kernel will correspond to a fitted curve (Equation 5, 6, 7, 8) as the preparation data needed for the actual prediction,” page 1235 column right paragraph 4 lines 1-9).
Huang does not appear to expressly teach a method wherein the computing platform compris[es] a computing device hosting a compiler.
Kim teaches a method wherein the computing platform compris[es] a computing device hosting a compiler (“FIG. 6 is a diagram illustrating an example of a process of generating training data according to an exemplary embodiment of the present disclosure. The latency prediction system 300 or the latency lookup table generator 310 may generate compiled single-neural-network-layer deep learning models 640 for an edge device 630 by compiling single-neural-network-layer deep learning models 610 in accordance with the edge device 630 using a compiler 620,” paragraph 0063 lines 1-9).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the estimate of Huang to comprise the compiler of Kim. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely compiling diverse models to sample real performance metrics (“FIG. 6 is a diagram illustrating an example of a process of generating training data according to an exemplary embodiment of the present disclosure. The latency prediction system 300 or the latency lookup table generator 310 may generate compiled single-neural-network-layer deep learning models 640 for an edge device 630 by compiling single-neural-network-layer deep learning models 610 in accordance with the edge device 630 using a compiler 620,” Kim paragraph 0063 lines 1-9). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 2, the rejection of claim 1 is incorporated. Huang/Kim further teaches a method wherein: application of the overhead latency estimator comprises multiplying the estimated latencies by scalars, the scalars being determined based, at least in part, on the trainable parameters (“the total kernel scheduling time during inference can be estimated as follows:
T
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e
=
∑
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=
1
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s
i
=
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⋅
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,” Huang page 1234 column right section “1) Single DNN workload prediction:” paragraph 2 lines 7-9).
As to dependent claim 3, the rejection of claim 1 is incorporated. Huang/Kim further teaches a method wherein:
the combination of estimated latencies of the individual kernels comprises a sum of individual estimated latencies associated with the individual kernels (“the latency of a single DNN is estimated as:
T
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f
=
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⋅
t
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+
∑
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⋅
s
i
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s
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g
p
u
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⋅
α
i
,” Huang page 1235 column right paragraph 1 lines 1-2, emphasis added); and
application of the overhead latency estimator comprises adding a latency overhead term to the sum (“the latency of a single DNN is estimated as:
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f
=
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⋅
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+
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⋅
s
i
n
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g
p
u
i
⋅
α
i
,” Huang page 1235 column right paragraph 1 lines 1-2, emphasis added).
As to dependent claim 7, the rejection of claim 1 is incorporated. Huang/Kim further teaches a method wherein the overhead latency estimate is determined further based, at least in part, on application of the overhead latency estimator to one or more parameters descriptive of a topology of the candidate neural network architecture (“When a deep learning model 710 is input, the latency prediction system 300 or the latency predictor 330 may divide the input deep learning model 710 into neural network layers and obtain a plurality of neural network layers 720,” Kim paragraph 0065 lines 4-8).
As to dependent claim 8, the rejection of claim 1 is incorporated. Huang/Kim further teaches a method wherein the individual kernels are executed by the candidate neural network architecture in a series, and wherein the combination of estimated latencies comprises a sum of latencies of the individual kernels (“the latency of a single DNN is estimated as:
T
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f
=
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⋅
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+
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n
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⋅
s
i
n
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g
p
u
i
⋅
α
i
,” Huang page 1235 column right paragraph 1 lines 1-2, emphasis added).
As to dependent claim 9, the rejection of claim 1 is incorporated. Huang/Kim further teaches a method wherein the sample neural networks and the candidate neural network architecture are selected from a neural network architecture search (NAS) space (“for each model to be predicted,” Huang page 1236 column right paragraph 3 line 2).
As to dependent claim 10, the rejection of claim 1 is incorporated. Huang/Kim further teaches a method wherein the trainable parameters are determined based, at least in part, on:
application of a first training epoch (“we perform a large number of inference tasks for different models. Each model is repeated several times,” Huang page 1235 column right paragraph 4 lines 1-2) to determine a first update of the trainable parameters based, at least in part, on a measured execution latency of a first sample neural network and an estimated execution latency of the first sample neural network (“The latency predictor may be a regression analysis model using a boosting algorithm. A boosting algorithm is an algorithm for improving prediction performance by sequentially training several weak learners and predicting the latency. For example, a gradient boosting algorithm employs a method of continuously reducing the error between an actual value and a predicted value of a previous model using a gradient and is known to show high performance,” Kim paragraph 0055 lines 1-9); and
application of a second training epoch (“we perform a large number of inference tasks for different models. Each model is repeated several times,” Huang page 1235 column right paragraph 4 lines 1-2) to determine a second update of the trainable parameters based, at least in part, on a measured execution latency of a second sample neural network and an estimated execution latency of the second sample neural network computed using the first update of the trainable parameters (“The latency predictor may be a regression analysis model using a boosting algorithm. A boosting algorithm is an algorithm for improving prediction performance by sequentially training several weak learners and predicting the latency. For example, a gradient boosting algorithm employs a method of continuously reducing the error between an actual value and a predicted value of a previous model using a gradient and is known to show high performance,” Kim paragraph 0055 lines 1-9).
As to dependent claim 11, the rejection of claim 10 is incorporated. Huang/Kim further teaches a method wherein the first update of the trainable parameters is determined based, at least in part, on a gradient of a loss function, the loss function computed based, at least in part, on the measured execution latency of the first sample neural network and the estimated execution latency of the first sample neural network (“The latency predictor may be a regression analysis model using a boosting algorithm. A boosting algorithm is an algorithm for improving prediction performance by sequentially training several weak learners and predicting the latency. For example, a gradient boosting algorithm employs a method of continuously reducing the error between an actual value and a predicted value of a previous model using a gradient and is known to show high performance,” Kim paragraph 0055 lines 1-9).
As to independent claim 12, Huang teaches a computing apparatus, comprising:
one or more memory devices (“memory,” page 1232 column left section “Abstract” line 12); and
one or more processors (“processors,” page 1232 column left section “Introduction” paragraph 2 line 6) coupled to the one or more memory devices to compute an estimate of a latency in an execution of a candidate neural network architecture to be implemented on a computing platform (“Then, the total latency
T
i
n
f
of DNN inference can be formulated as:
T
i
n
f
=
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s
c
h
e
+
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e
x
e
c
=
∑
i
=
1
M
(
s
i
+
t
i
)
,” page 1234 column right section “1) Single DNN workload prediction:” lines 6-8), [] the estimate of the latency in the execution of the candidate neural network architecture to be based, at least in part, on:
a combination of estimated latencies of individual kernels defined in the computing platform and to be executed by the candidate neural network architecture (“the execution latency of each kernel can be denoted by t1, t2…tm,” page 1234 column right section “1) Single DNN workload prediction:” lines 5-6); and
application of an overhead latency estimator to design features of the candidate neural network architecture to determine an overhead latency estimate (“the scheduling latency of each kernel can be denoted by s1, s2…sm,” page 1234 column right section “1) Single DNN workload prediction:” lines 4-5), wherein the overhead latency estimator comprises trainable parameters determined from measured latencies of execution of sample neural networks on the computing platform (“In data preparation, we perform a large number of inference tasks for different models. Each model is repeated several times with varying amounts of GPU resources assigned (e.g., 10%, 25%, 50%, 90%) to obtain the values of
S
i
n
s
i
,
S
g
p
u
i
,
r
s
m
, and α •
r
s
m
for each type of kernel as sample points. Then we perform a least-squares fit for these parameters of each kernel separately, and eventually,
S
i
n
s
i
,
S
g
p
u
i
,
r
s
m
, and α •
r
s
m
of each kernel will correspond to a fitted curve (Equation 5, 6, 7, 8) as the preparation data needed for the actual prediction,” page 1235 column right paragraph 4 lines 1-9).
Huang does not appear to expressly teach an apparatus wherein the computing platform compris[es] a computing device hosting a compiler.
Kim teaches an apparatus wherein the computing platform compris[es] a computing device hosting a compiler (“FIG. 6 is a diagram illustrating an example of a process of generating training data according to an exemplary embodiment of the present disclosure. The latency prediction system 300 or the latency lookup table generator 310 may generate compiled single-neural-network-layer deep learning models 640 for an edge device 630 by compiling single-neural-network-layer deep learning models 610 in accordance with the edge device 630 using a compiler 620,” paragraph 0063 lines 1-9).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the estimate of Huang to comprise the compiler of Kim. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely compiling diverse models to sample real performance metrics (“FIG. 6 is a diagram illustrating an example of a process of generating training data according to an exemplary embodiment of the present disclosure. The latency prediction system 300 or the latency lookup table generator 310 may generate compiled single-neural-network-layer deep learning models 640 for an edge device 630 by compiling single-neural-network-layer deep learning models 610 in accordance with the edge device 630 using a compiler 620,” Kim paragraph 0063 lines 1-9). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 13, the rejection of claim 12 is incorporated. Huang/Kim further teaches an apparatus wherein: application of the overhead latency estimator comprises multiplying the estimated latencies by scalars, the scalars being determined based, at least in part, on the trainable parameters (“the total kernel scheduling time during inference can be estimated as follows:
T
s
c
h
e
=
∑
i
=
1
M
s
i
=
M
⋅
t
s
c
h
e
b
⋅
(
g
/
100
)
,” Huang page 1234 column right section “1) Single DNN workload prediction:” paragraph 2 lines 7-9).
As to dependent claim 14, the rejection of claim 12 is incorporated. Huang/Kim further teaches an apparatus wherein:
the combination of estimated latencies of the individual kernels comprises a sum of individual estimated latencies associated with the individual kernels (“the latency of a single DNN is estimated as:
T
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n
f
=
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⋅
t
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+
∑
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i
n
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⋅
s
i
n
s
i
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g
p
u
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⋅
α
i
,” Huang page 1235 column right paragraph 1 lines 1-2, emphasis added); and
application of the overhead latency estimator comprises adding a latency overhead term to the sum (“the latency of a single DNN is estimated as:
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f
=
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⋅
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+
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⋅
s
i
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s
i
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g
p
u
i
⋅
α
i
,” Huang page 1235 column right paragraph 1 lines 1-2, emphasis added).
As to dependent claim 18, the rejection of claim 12 is incorporated. Huang/Kim further teaches an apparatus wherein the overhead latency estimate is determined further based, at least in part, on application of the overhead latency estimator to one or more parameters descriptive of a topology of the candidate neural network architecture (“When a deep learning model 710 is input, the latency prediction system 300 or the latency predictor 330 may divide the input deep learning model 710 into neural network layers and obtain a plurality of neural network layers 720,” Kim paragraph 0065 lines 4-8).
As to dependent claim 19, the rejection of claim 12 is incorporated. Huang/Kim further teaches an apparatus wherein the trainable parameters are determined based, at least in part, on:
application of a first training epoch (“we perform a large number of inference tasks for different models. Each model is repeated several times,” Huang page 1235 column right paragraph 4 lines 1-2) to determine a first update of the trainable parameters based, at least in part, on a measured execution latency of a first sample neural network and an estimated execution latency of the first sample neural network (“The latency predictor may be a regression analysis model using a boosting algorithm. A boosting algorithm is an algorithm for improving prediction performance by sequentially training several weak learners and predicting the latency. For example, a gradient boosting algorithm employs a method of continuously reducing the error between an actual value and a predicted value of a previous model using a gradient and is known to show high performance,” Kim paragraph 0055 lines 1-9); and
application of a second training epoch (“we perform a large number of inference tasks for different models. Each model is repeated several times,” Huang page 1235 column right paragraph 4 lines 1-2) to determine a second update of the trainable parameters based, at least in part, on a measured execution latency of a second sample neural network and an estimated execution latency of the second sample neural network computed using the first update of the trainable parameters (“The latency predictor may be a regression analysis model using a boosting algorithm. A boosting algorithm is an algorithm for improving prediction performance by sequentially training several weak learners and predicting the latency. For example, a gradient boosting algorithm employs a method of continuously reducing the error between an actual value and a predicted value of a previous model using a gradient and is known to show high performance,” Kim paragraph 0055 lines 1-9).
As to independent claim 20, Huang teaches an article comprising:
a storage medium (“memory,” page 1232 column left section “Abstract” line 12) comprising computer-readable instructions stored thereon, the computer-readable instructions to be executable by one or more processors (“processors,” page 1232 column left section “Introduction” paragraph 2 line 6) of a computing device to:
compute an estimate of a latency in an execution of a candidate neural network architecture to be implemented on a computing platform (“Then, the total latency
T
i
n
f
of DNN inference can be formulated as:
T
i
n
f
=
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c
h
e
+
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e
x
e
c
=
∑
i
=
1
M
(
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+
t
i
)
,” page 1234 column right section “1) Single DNN workload prediction:” lines 6-8), [] the estimate of the latency in the execution of the candidate neural network architecture to be based, at least in part, on:
a combination of estimated latencies of individual kernels defined in the computing platform and to be executed by the candidate neural network architecture (“the execution latency of each kernel can be denoted by t1, t2…tm,” page 1234 column right section “1) Single DNN workload prediction:” lines 5-6); and
application of an overhead latency estimator to design features of the candidate neural network architecture to determine an overhead latency estimate (“the scheduling latency of each kernel can be denoted by s1, s2…sm,” page 1234 column right section “1) Single DNN workload prediction:” lines 4-5), wherein the overhead latency estimator comprises trainable parameters determined from measured latencies of execution of sample neural networks on the computing platform (“In data preparation, we perform a large number of inference tasks for different models. Each model is repeated several times with varying amounts of GPU resources assigned (e.g., 10%, 25%, 50%, 90%) to obtain the values of
S
i
n
s
i
,
S
g
p
u
i
,
r
s
m
, and α •
r
s
m
for each type of kernel as sample points. Then we perform a least-squares fit for these parameters of each kernel separately, and eventually,
S
i
n
s
i
,
S
g
p
u
i
,
r
s
m
, and α •
r
s
m
of each kernel will correspond to a fitted curve (Equation 5, 6, 7, 8) as the preparation data needed for the actual prediction,” page 1235 column right paragraph 4 lines 1-9).
Huang does not appear to expressly teach an article wherein the computing platform compris[es] a computing device hosting a compiler.
Kim teaches an article wherein the computing platform compris[es] a computing device hosting a compiler (“FIG. 6 is a diagram illustrating an example of a process of generating training data according to an exemplary embodiment of the present disclosure. The latency prediction system 300 or the latency lookup table generator 310 may generate compiled single-neural-network-layer deep learning models 640 for an edge device 630 by compiling single-neural-network-layer deep learning models 610 in accordance with the edge device 630 using a compiler 620,” paragraph 0063 lines 1-9).
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the estimate of Huang to comprise the compiler of Kim. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely compiling diverse models to sample real performance metrics (“FIG. 6 is a diagram illustrating an example of a process of generating training data according to an exemplary embodiment of the present disclosure. The latency prediction system 300 or the latency lookup table generator 310 may generate compiled single-neural-network-layer deep learning models 640 for an edge device 630 by compiling single-neural-network-layer deep learning models 610 in accordance with the edge device 630 using a compiler 620,” Kim paragraph 0063 lines 1-9). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
Claims 4-6 and 15-17 are rejected under 35 U.S.C. § 103 as being unpatentable over Huang in view of Kim and Ponomarev et al. (“Latency Estimation Tool and Investigation of Neural Networks Inference on Mobile GPU,” 22 August 2021, https://www.mdpi.com/2073-431X/10/8/104, hereinafter Ponomarev).
As to dependent claim 4, the rejection of claim 1 is incorporated. Huang/Kim further teaches a method wherein the overhead latency estimator comprises: a second neural network having parameters trained to map sample neural networks of multiple neural network search spaces to the input tensor (“When a deep learning model 710 is input, the latency prediction system 300 or the latency predictor 330 may divide the input deep learning model 710 into neural network layers and obtain a plurality of neural network layers 720. Each of the plurality of neural network layers 720 may be input to a latency predictor 730,” Kim paragraph 0065 lines 4-9).
Huang/Kim does not appear to expressly teach a method wherein the overhead latency estimator comprises: a first neural network to compute an estimated latency based, at least in part, on an input tensor.
Ponomarev teaches a method wherein the overhead latency estimator comprises: a first neural network to compute an estimated latency based, at least in part, on an input tensor (“GCN latency predictor consists of a graph convolutional network which learns models for graph-structured data [31]. Given a graph g = (V, E), where V is a set of N nodes with D features, and E is a set of edges, a GCN takes as input a feature description X ∊ ℝN×D and a description of the graph structure as an adjacency matrix A ∊ ℝN×N,” page 11 section “4.4. Graph Convolutional Network (GCN) for Latency Prediction” paragraph 2 lines 1-4); and
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the estimator of Huang/Kim to comprise the first neural network of Ponomarev. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely using a neural network to compute an estimated latency based, at least in part, on an input tensor (“GCN latency predictor consists of a graph convolutional network which learns models for graph-structured data [31]. Given a graph g = (V, E), where V is a set of N nodes with D features, and E is a set of edges, a GCN takes as input a feature description X ∊ ℝN×D and a description of the graph structure as an adjacency matrix A ∊ ℝN×N,” Ponomarev page 11 section “4.4. Graph Convolutional Network (GCN) for Latency Prediction” paragraph 2 lines 1-4). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 5, the rejection of claim 4 is incorporated. Huang/Kim/Ponomarev further teaches a method wherein parameters of the first (“GCN latency predictor consists of a graph convolutional network which learns models for graph-structured data [31]. Given a graph g = (V, E), where V is a set of N nodes with D features, and E is a set of edges, a GCN takes as input a feature description X ∊ ℝN×D and a description of the graph structure as an adjacency matrix A ∊ ℝN×N,” Ponomarev page 11 section “4.4. Graph Convolutional Network (GCN) for Latency Prediction” paragraph 2 lines 1-4) and second (“When a deep learning model 710 is input, the latency prediction system 300 or the latency predictor 330 may divide the input deep learning model 710 into neural network layers and obtain a plurality of neural network layers 720. Each of the plurality of neural network layers 720 may be input to a latency predictor 730,” Kim paragraph 0065 lines 4-9) neural networks are trained separately.
As to dependent claim 6, the rejection of claim 4 is incorporated. Huang/Kim/Ponomarev further teaches a method wherein the multiple neural network search spaces include neural networks of different depths (“When a deep learning model 710 is input, the latency prediction system 300 or the latency predictor 330 may divide the input deep learning model 710 into neural network layers and obtain a plurality of neural network layers 720. Each of the plurality of neural network layers 720 may be input to a latency predictor 730,” Kim paragraph 0065 lines 4-9).
As to dependent claim 15, the rejection of claim 12 is incorporated. Huang/Kim further teaches an apparatus wherein the overhead latency estimator comprises: a second neural network having parameters trained to map sample neural networks of multiple neural network search spaces to the input tensor (“When a deep learning model 710 is input, the latency prediction system 300 or the latency predictor 330 may divide the input deep learning model 710 into neural network layers and obtain a plurality of neural network layers 720. Each of the plurality of neural network layers 720 may be input to a latency predictor 730,” Kim paragraph 0065 lines 4-9).
Huang/Kim does not appear to expressly teach an apparatus wherein the overhead latency estimator comprises: a first neural network to compute an estimated latency based, at least in part, on an input tensor.
Ponomarev teaches an apparatus wherein the overhead latency estimator comprises: a first neural network to compute an estimated latency based, at least in part, on an input tensor (“GCN latency predictor consists of a graph convolutional network which learns models for graph-structured data [31]. Given a graph g = (V, E), where V is a set of N nodes with D features, and E is a set of edges, a GCN takes as input a feature description X ∊ ℝN×D and a description of the graph structure as an adjacency matrix A ∊ ℝN×N,” page 11 section “4.4. Graph Convolutional Network (GCN) for Latency Prediction” paragraph 2 lines 1-4); and
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the estimator of Huang/Kim to comprise the first neural network of Ponomarev. (1) The Examiner finds that the prior art included each claim element listed above, although not necessarily in a single prior art reference, with the only difference between the claimed invention and the prior art being the lack of actual combination of the elements in a single prior art reference. (2) The Examiner finds that one of ordinary skill in the art could have combined the elements as claimed by known software development methods, and that in combination, each element merely performs the same function as it does separately. (3) The Examiner finds that one of ordinary skill in the art would have recognized that the results of the combination were predictable, namely using a neural network to compute an estimated latency based, at least in part, on an input tensor (“GCN latency predictor consists of a graph convolutional network which learns models for graph-structured data [31]. Given a graph g = (V, E), where V is a set of N nodes with D features, and E is a set of edges, a GCN takes as input a feature description X ∊ ℝN×D and a description of the graph structure as an adjacency matrix A ∊ ℝN×N,” Ponomarev page 11 section “4.4. Graph Convolutional Network (GCN) for Latency Prediction” paragraph 2 lines 1-4). Therefore, the rationale to support a conclusion that the claim would have been obvious is that the combining prior art elements according to known methods to yield predictable results to one of ordinary skill in the art. See MPEP § 2143(I)(A).
As to dependent claim 16, the rejection of claim 15 is incorporated. Huang/Kim/Ponomarev further teaches an apparatus wherein parameters of the first (“GCN latency predictor consists of a graph convolutional network which learns models for graph-structured data [31]. Given a graph g = (V, E), where V is a set of N nodes with D features, and E is a set of edges, a GCN takes as input a feature description X ∊ ℝN×D and a description of the graph structure as an adjacency matrix A ∊ ℝN×N,” Ponomarev page 11 section “4.4. Graph Convolutional Network (GCN) for Latency Prediction” paragraph 2 lines 1-4) and second (“When a deep learning model 710 is input, the latency prediction system 300 or the latency predictor 330 may divide the input deep learning model 710 into neural network layers and obtain a plurality of neural network layers 720. Each of the plurality of neural network layers 720 may be input to a latency predictor 730,” Kim paragraph 0065 lines 4-9) neural networks are trained separately.
As to dependent claim 17, the rejection of claim 15 is incorporated. Huang/Kim/Ponomarev further teaches an apparatus wherein the multiple neural network search spaces include neural networks of different depths (“When a deep learning model 710 is input, the latency prediction system 300 or the latency predictor 330 may divide the input deep learning model 710 into neural network layers and obtain a plurality of neural network layers 720. Each of the plurality of neural network layers 720 may be input to a latency predictor 730,” Kim paragraph 0065 lines 4-9).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
US 2020/0342291 A1 disclosing estimating latency of neural network kernels
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
In the interests of compact prosecution, Applicant is invited to contact the examiner via electronic media pursuant to USPTO policy outlined MPEP § 502.03. All electronic communication must be authorized in writing. Applicant may wish to file an Internet Communications Authorization Form PTO/SB/439. Applicant may wish to request an interview using the Interview Practice website: http://www.uspto.gov/patent/laws-and-regulations/interview-practice.
Applicant is reminded Internet e-mail may not be used for communication for matters under 35 U.S.C. § 132 or which otherwise require a signature. A reply to an Office action may NOT be communicated by Applicant to the USPTO via Internet e-mail. If such a reply is submitted by Applicant via Internet e-mail, a paper copy will be placed in the appropriate patent application file with an indication that the reply is NOT ENTERED. See MPEP § 502.03(II).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ryan Barrett whose telephone number is 571 270 3311. The examiner can normally be reached 9:00am to 5:30pm.
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/Ryan Barrett/
Primary Examiner, Art Unit 2148