DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Allowable Subject Matter
Claims 5, 9, 11-13, 18, 22, and 24-26 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter.
Claims 1-4, 6-8, 10, 14-17, 19-21, 23, and 27-28 are rejected under 35 U.S.C. 101.
As per claim 1, the claim recites a method, therefore is a process.
“ . . . determining that a temperature measured for the first processing unit exceeds a threshold temperature . . . selecting, based on one or more operating parameters for the computing device, a second processing unit of the computing device to use in executing operations in a second portion of the machine learning model … “ These limitations, as drafted, are processes that, under its broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of generic computer components. Thus, the claim recites a mental process.
The limitation of “measuring a temperature for each of a plurality of locations … ”, amounts to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g); this limitation is also a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g). The claim is directed to the abstract idea.
As discussed above, “measuring a temperature for each of a plurality of locations …”, amounts to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g); this limitation is also a mere generic transmission and presentation of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g). “scheduling execution of operations in the second portion of the machine learning model on the second processing unit” “ is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. See Maity. The claim is ineligible.
As per claim 2, see rejection on claim 1. “wherein the first portion of the machine learning model and the second portion of the machine learning model comprise layers of a neural network configured for execution on a same set of processing units “ is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. See AAPA. The claim is ineligible.
As per claim 3, see rejection on claim 1. “wherein the first portion of the machine learning model is a member of a first set of layers configured for execution on a first set of processing units of the computing device and the second portion of the machine learning model is a member of a second set of layers configured for execution on a second set of processing units of the computing device“ is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. See Guo. The claim is ineligible.
As per claim 4, see rejection on claim 1. “ wherein the first set of layers comprise a set of layers configured with a first set of quantization parameters “ is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. See Guo. The claim is ineligible.
As per claim 6, see rejection on claim 3. “ wherein the first set of processing units comprises a neural processing unit (NPU), a digital signal processor (DSP), and a plurality of central processing unit (CPU) cores. “ is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. See AAPA. The claim is ineligible.
As per claim 7, see rejection on claim 6. “ wherein the second set of processing units comprises the plurality of CPU cores and a plurality of graphics processing unit (GPU) processors“ is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. See AAPA. The claim is ineligible.
As per claim 8, see rejection on claim 1. “ wherein selecting the second processing unit is further based on a ranking of types of processing units for executing operations in the second portion of the machine learning model“ is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. See Maity. The claim is ineligible.
As per claim 10, see rejection on claim 1. “ wherein the one or more operating parameters comprise one or more of a distance between one or more processing units and the first processing unit, a temperature of the one or more processing units, or a current load on the one or more processing units. “ is simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception - see MPEP 2106.05(d) and Berkheimer Memo. See Maity. The claim is ineligible.
As per claim 14, see rejection on claim 1.
As per claim 15, see rejection on claim 2.
As per claim 16, see rejection on claim 3.
As per claim 17, see rejection on claim 4.
As per claim 19, see rejection on claim 6.
As per claim 20, see rejection on claim 7.
As per claim 21, see rejection on claim 8.
As per claim 23, see rejection on claim 10.
As per claim 27, see rejection on claim 1.
As per claim 28, see rejection on claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 8, 10, 14-15, 21, 23, and 27-28 are rejected under 35 U.S.C. 103 as being unpatentable over Maity et al (Maity, Srijeeta, et al. "Thermal-aware adaptive platform management for heterogeneous embedded systems." ACM Transactions on Embedded Computing Systems (TECS) 20.5s (2021): 1-28) in view of Applicant Admitted Prior Art (Background, Spec) (hereinafter AAPA).
As per claim 1, Maity teaches:
A method implemented on a computing device having multiple processing units, comprising:
during execution of operations in a first portion of an application on a first processing unit of the computing device, measuring a temperature for each of a plurality of locations on the computing device (Maity, Table 1. Recovery Action Selection—under BRI, , measuring a temperature for each of a plurality of locations on the computing device can be measuring temps to determine thermal violations in devices);
determining that a temperature measured for the first processing unit exceeds a threshold temperature, selecting, based on one or more operating parameters for the computing device, a second processing unit of the computing device to use in executing operations in a second portion of the application (Maity, Table 1. Recovery Action Selection—under BRI, a second processing unit of the computing device can be device C or G that the task is migrating to); and
scheduling execution of operations in the second portion of the application on the second processing unit device (Maity, Table 1. Recovery Action Selection).
Maity does not expressly teach:
wherein the application is a machine learning model;
However, AAPA discloses:
wherein the application is a machine learning model (AAPA, [0002]);
Both AAPA and Maity pertain to the art of task scheduling.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to process AAPA’s ML model because machine learning (ML) models provide major benefits by enabling automation, data-driven decision-making, and pattern recognition at scale.
As per claim 2, Maity/AAPA teaches:
The method of Claim 1 (see rejection on claim 1), wherein the first portion of the machine learning model and the second portion of the machine learning model comprise layers of a neural network configured for execution on a same set of processing units (AAPA, [0002]).
As per claim 8, Maity/AAPA teaches:
The method of Claim 1 (see rejection on claim 1), wherein selecting the second processing unit is further based on a ranking of types of processing units for executing operations in the second portion of the machine learning model (Maity, Table 1. Recovery Action Selection—under BRI, a type can type C or G).
As per claim 10, Maity/AAPA teaches:
The method of Claim 1 (See rejection on claim 1), wherein the one or more operating parameters comprise one or more of a distance between one or more processing units and the first processing unit, a temperature of the one or more processing units, or a current load on the one or more processing units (Maity, Table 1. Recovery Action Selection—under BRI, a current load on the one or more processing units can be utilization).
As per claim 14, see rejection on claim 1.
As per claim 15, see rejection on claim 2.
As per claim 21, see rejection on claim 1.
As per claim 23, see rejection on claim 10.
As per claim 27, see rejection on claim 1.
As per claim 28, see rejection on claim 1.
Claims 3-4, 6-7, 16-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Maity/AAPA as applied above, and further in view of Guo et al (Guo, Yanfei, Palden Lama, and Xiaobo Zhou. "Automated and agile server parameter tuning with learning and control." 2012 IEEE 26th International Parallel and Distributed Processing Symposium. IEEE, 2012) (hereinafter Guo).
As per claim 3, Maity/AAPA teaches:
The method of Claim 1 (see rejection on claim 1), a set is a first set of processing units of the computing device, and a second set of processing units of the computing device.
Maity/AAPA does not expressly teach:
wherein the first portion of the machine learning model is a member of a first set of layers configured for execution on the set and the second portion of the machine learning model is a member of a second set of layers configured for execution on the set.
However, Guo discloses:
wherein the first portion of the machine learning model is a member of a first set of layers configured for execution on the set (Guo, IV. APPROACHES WITH LEARNING AND CONTROL, A. An Enriched Neural Fuzzy Control based Approach, 2) Design of the Neural Fuzzy Controller)
and the second portion of the machine learning model is a member of a second set of layers configured for execution on the set (Guo, IV. APPROACHES WITH LEARNING AND CONTROL, A. An Enriched Neural Fuzzy Control based Approach, 2) Design of the Neural Fuzzy Controller).
Both Guo and Maity/AAPA pertain to the art of task scheduling.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to use Guo’s method to execute ML on five-layer neural network because a 5-layer neural network provides better pattern recognition, feature hierarchy, and higher accuracy than shallow networks by stacking multiple hidden layers to process complex data
As per claim 4, Maity/AAPA/Guo teaches:
The method of Claim 3 (see rejection on claim 3), wherein the first set of layers comprise a set of layers configured with a first set of quantization parameters ( Guo, IV. APPROACHES WITH LEARNING AND CONTROL, A. An Enriched Neural Fuzzy Control based Approach, 2) Design of the Neural Fuzzy Controller—under BRI, quantization parameters can be activation functions).
As per claim 6, Maity/AAPA/Guo teaches:
The method of Claim 3, wherein the first set of processing units comprises a neural processing unit (NPU), a digital signal processor (DSP), and a plurality of central processing unit (CPU) cores (AAPA, [0002]).
As per claim 7, Maity/AAPA/Guo teaches:
The method of Claim 6 (see rejection on claim 6), wherein the second set of processing units comprises the plurality of CPU cores and a plurality of graphics processing unit (GPU) processors (AAPA, [0002]).
As per claim 16, see rejection on claim 3.
As per claim 17, see rejection on claim 4.
As per claim 19, see rejection on claim 6.
As per claim 20, see rejection on claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2023/0252264 teaches a method of implementing multi-layer neural network.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLIE SUN whose telephone number is (571)270-5100. The examiner can normally be reached 9AM-5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Vital can be reached at (571) 272-4215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHARLIE SUN/Primary Examiner, Art Unit 2198
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