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 .
Claims 1-15 are presented for examination.
Specification
The disclosure is objected to because of the following informalities:
Paragraph 5 recites "infulncing the user experience," which should instead recite "influencing the user experience."
Paragraph 34 recites “a point of service (POS) machine, a point of service (POS) machine, a point of sale (POS) machine,” which redundantly recites "a point of service (POS) machine" twice.
Paragraph 35 recites "a dual-mode machine learning model programming module 120," which should instead read "dual-model machine learning model programming module 120."
Appropriate correction is required.
Claim Objections
Claims 1-15 are objected to because of the following informalities:
Claim 1 recites “An energy consumption control method, comprising: performing following steps by a processor.” Examiner suggests the limitation to read “performing a series of steps by a processor.”
Claim 7 recites “the first performance parameter comprises one of a power, a thermal design power, and a combination thereof, and the second and third performance parameters comprise one of a load and a frequency,” which should read “the first performance parameter comprises one of a power, a thermal design power, or a combination thereof, and the second and third performance parameters comprise one of a load or a frequency.”
Claims 1, 9, and 13 recite "detecting and collecting a performance data," which should instead read "detecting and collecting performance data."
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a data collection programming module” in claim 9.
"a dual-model machine learning model programming module" in claim 9.
"an automatic control programming module" in claim 9.
Regarding the invocation of 112f, see rejections under 35 U.S.C. 112(a)-(b) infra.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 9-12 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The claim limitation "data collection programming module" invokes 35 U.S.C 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. At most, the function of the data collection programming module is described in paragraphs 36 and 38, which describes that the module is configured to continuously detect and collect the performance data by reading performance parameter values through a generic API call (e.g., the GetSystemPowerStatus function of the Win32 API) and averaging sampled values without disclosing an algorithm (e.g., mathematical formula, sequence of steps) sufficient to perform the entire claimed function.
The claim limitation "dual-model machine learning model programming module" invokes 35 U.S.C 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. At most, the function of the dual-model machine learning model programming module is described in paragraphs 41 and 44, which describe that the module is configured to execute a dual-model machine learning model to “perform short-term and long-term predictions for the power or the TDP of the processor” and “[integrate] two separate and independent machine learning models” without providing any algorithm (e.g., mathematical formula, sequence of steps) for how the first and second sub-machine learning models are integrated into a single machine learning model and how the model processes the input to generate the predicted outputs for the short-term and long-term predictions.
The claim limitation "automatic control programming module" invokes 35 U.S.C 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. At most, the function of the automatic control programming module is described in paragraphs 60 and 61, which describe that the module is configured to implement a fuzzy feedback control mechanism to adjust the TDP prediction value (first performance parameter) at a high level of generality, reciting that the TDP prediction value is fine-tuned toward an optimal point without disclosing an algorithm (e.g., mathematical formula, sequence of steps) specifying the membership functions applied, how the fuzzy feedback control mechanism is performed, and how the optimal point is determined to perform the entire claimed function.
Thus, the specification does not disclose a definite structure for the "data collection programming module," the " dual-model machine learning model programming module," and the “automatic control programming module,” and does not provide an accompanying algorithm to perform the claimed specific computer function. It has been recognized by the courts that "merely restating a function associated with a means-plus-function limitation is insufficient to provide the corresponding structure for definiteness" (see MPEP 2181 (IV)). Therefore, it is unclear whether Applicant had possession of the claimed invention as of the effective filing date. For the purposes of further examination, the "data collection programming module," the "dual-model machine learning model programming module," and the “automatic control programming module” will be interpreted as part of a computer processor.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 9-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim limitation "data collection programming module" invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. At most, the function of the data collection programming module is described in paragraphs 36 and 38, which describe that the module is configured to continuously detect and collect the performance data by reading performance parameter values through a generic API call (e.g., the GetSystemPowerStatus function of the Win32 API) and averaging sampled values without disclosing an algorithm (e.g., mathematical formula, sequence of steps) sufficient to perform the entire claimed function.
Claim limitation "dual-model machine learning model programming module" invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. At most, the function of the dual-model machine learning model programming module is described in paragraphs 41 and 44, which describe that the module is configured to execute a dual-model machine learning model to “perform short-term and long-term predictions for the power or the TDP of the processor” and “[integrate] two separate and independent machine learning models” without providing any algorithm (e.g., mathematical formula, sequence of steps) for how the first and second sub-machine learning models are integrated into a single machine learning model and how the model processes the input to generate the predicted outputs for the short-term and long-term predictions.
Claim limitation "automatic control programming module" invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. At most, the function of the automatic control programming module is described in paragraphs 60 and 61, which describe that the module is configured to implement a fuzzy feedback control mechanism to adjust the TDP prediction value (first performance parameter) at a high level of generality, reciting that the TDP prediction value is fine-tuned toward an optimal point without disclosing an algorithm (e.g., mathematical formula, sequence of steps) specifying the membership functions applied, how the fuzzy feedback control mechanism is performed, and how the optimal point is determined to perform the entire claimed function.
Thus, the specification does not disclose a definite structure for the "data collection programming module," the "dual-model machine learning model programming module," and the “automatic control programming module” and does not provide an accompanying algorithm to perform the claimed specific computer functions. The scope of these functional limitations, therefore, encompasses any and all software that performs the claimed functions, without limitation to a specific disclosed structure or algorithm. It has been recognized by the courts that "merely restating a function associated with a means-plus-function limitation is insufficient to provide the corresponding structure for definiteness" (see MPEP 2181 (IV)). Therefore, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 9-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 9 recites an "energy consumption control computer program product, which is embodied on a non-transitory computer-readable storage medium and loaded and executed by a processor," where the claim is directed to the program. Although a non-transitory medium and a processor are recited, the transitional phrase "comprising" sets forth only the programming modules. The claim is therefore directed to the computer program itself rather than to the non-transitory medium as a statutory manufacture. Paragraph 10 of the instant specification describes the product as embodied on a non-transitory computer-readable storage medium, yet the claim recites the programming modules as the claimed subject matter. Applicant may overcome this rejection by amending claim 9 to recite "an energy consumption control computer program product comprising a non-transitory computer-readable storage medium storing instructions that, when executed by the processor, cause the processor to [perform the recited steps]" to clearly claim a statutory manufacture.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an
abstract idea without significantly more.
Claim 1
Step 1: The claim recites a method; therefore, it is directed to the statutory category of a process.
Step2A Prong 1: The claim recites, inter alia:
continuously detecting and collecting a performance data … wherein the performance data comprises a first performance parameter, a second performance parameter, and a third performance parameter: This limitation recites a mental process because it involves observing the performance data by detecting and collecting the first, second, and third performance parameters, which can be performed in the human mind or by pen and paper.
…to predict the first performance parameter based on the performance data: This limitation recites a mental process because it involves predicting/evaluating the first performance parameter of the processor from the performance data, which can be performed in the human mind or by pen and paper.
…to adjust the first performance parameter based on the detected second and third performance parameters: This limitation recites a mental process because it involves evaluating the detected second and third performance parameters and adjusting the first performance parameter, which can be performed in the human mind or by pen and paper.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
performing following steps by a processor: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
executing a dual-model machine learning model: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
implementing a fuzzy feedback control mechanism: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
performing following steps by a processor: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
executing a dual-model machine learning model: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
implementing a fuzzy feedback control mechanism: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
The elements in combination as an ordered whole still do not amount to significantly more than the judicial exception (i.e., the abstract ideas of mental processes for detecting and collecting performance data, predicting a performance parameter, and adjusting that parameter based on other detected performance parameters). The recitation of the processor, the dual-model machine learning model, and the fuzzy feedback control mechanism merely indicate a technological environment in which the abstract ideas are applied, without improving the functioning of a computer or the energy consumption control system itself.
Therefore, the claim as a whole remains focused on the abstract idea and fails Step 2B of the eligibility analysis.
Claim 2
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
…to predict a first period first performance parameter in a first period: This limitation recites a mental process because it involves predicting/evaluating a performance parameter over a first period, which can be performed in the human mind or by pen and paper.
…to predict a second period first performance parameter in a second period: This limitation recites a mental process because it involves predicting/evaluating a performance parameter over a second period, which can be performed in the human mind or by a human using pen and paper.
selectively correcting the first period first performance parameter based on the second period first performance parameter to use as the first performance parameter: This limitation recites a mental process because it involves evaluating one predicted value against another and correcting it accordingly, which can be performed in the human mind or by a human using pen and paper.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
selectively executing a first sub-machine learning model comprised in the dual model machine learning model: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
selectively executing a second sub-machine learning model comprised in the dual model machine learning model: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
selectively executing a first sub-machine learning model comprised in the dual model machine learning model: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
selectively executing a second sub-machine learning model comprised in the dual model machine learning model: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 3
Step 1: A process, as above.
Step2A Prong 1: The claim recites, inter alia:
to predict a first period first performance parameter in a first period and a second period first performance parameter in a second period and to correct the first period first performance parameter based on the second period first performance parameter to use as the first performance parameter, wherein the first period and the second period have different durations: This limitation recites a mental process because it involves predicting performance parameters over two periods and correcting one based on the other, which can be performed in the human mind or by pen and paper.
to add a fine-tuning value to the first performance parameter based on the detected second and third performance parameters to adjust the first performance parameter: This limitation recites a mental process because it involves the evaluation/judgement/opinion of the detected second and third performance parameters and adds a fine-tuning value to adjust the first performance parameter, which can be performed in the human mind or by pen and paper.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
selectively executing the dual-model machine learning model: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
implementing the fuzzy feedback control mechanism: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
selectively executing the dual-model machine learning model: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
implementing the fuzzy feedback control mechanism: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 4
Step 1: A process, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 2, which recites an abstract idea.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
wherein the first sub-machine learning model comprises one of a neural network model, a deep neural network model, a convolutional neural network model, a multilayer perceptron model, a moving average model, an exponential smoothing model, an autoregressive model, a vector autoregressive model, an autoregressive moving average model, an integrated moving average autoregressive model, a regression tree model, a growth model, a latent growth curve model, a latent growth model, a Fourier model, a Fourier series model, a trend model, a prophet model, and a combination thereof: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
wherein the first sub-machine learning model comprises one of a neural network model, a deep neural network model, a convolutional neural network model, a multilayer perceptron model, a moving average model, an exponential smoothing model, an autoregressive model, a vector autoregressive model, an autoregressive moving average model, an integrated moving average autoregressive model, a regression tree model, a growth model, a latent growth curve model, a latent growth model, a Fourier model, a Fourier series model, a trend model, a prophet model, and a combination thereof: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 5
Step 1: A process, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 2, which recites an abstract idea.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
wherein the second sub-machine learning model comprises one of a neural network model, a deep neural network model, a convolutional neural network model, a recurrent neural network model, a gated recurrent unit model, a long short-term memory model, a multilayer perceptron model, and a combination thereof: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
wherein the second sub-machine learning model comprises one of a neural network model, a deep neural network model, a convolutional neural network model, a recurrent neural network model, a gated recurrent unit model, a long short-term memory model, a multilayer perceptron model, and a combination thereof: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 6
Step 1: A process, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 3, which recites an abstract idea.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
wherein the fine-tuning value has a numerical value in a range between ±2% of a value of the first performance parameter: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
wherein the fine-tuning value has a numerical value in a range between ±2% of a value of the first performance parameter: The limitation amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 7
Step 1: A process, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 1, which recites an abstract idea.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
wherein the first performance parameter comprises one of a power, a thermal design power, and a combination thereof, and the second and third performance parameters comprise one of a load and a frequency: The limitation merely describes the type of data being processed and thus amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
wherein the first performance parameter comprises one of a power, a thermal design power, and a combination thereof, and the second and third performance parameters comprise one of a load and a frequency: The limitation merely describes the type of data being processed and thus amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 8
Step 1: A process, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 1, which recites an abstract idea.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
wherein the performance data consists of time series of the first, second, and third performance parameters: The limitation merely describes the type of data being processed and thus amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself (MPEP 2106.05(h)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
wherein the performance data consists of time series of the first, second, and third performance parameters: The limitation merely describes the type of data being processed and thus amounts to merely indicating a field of use or technological environment in which to apply a judicial exception. This does not amount to significantly more than the exception itself which cannot provide inventive concept (MPEP 2106.05(h)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 9
Step 1: The claim recites a computer program product; therefore, it is directed to the statutory category of an article of manufacture.
Step2A Prong 1: The claim recites, inter alia:
continuously detect and collect a performance data … wherein the performance data comprises a first performance parameter, a second performance parameter, and a third performance parameter: This limitation recites a mental process because it involves observing the performance data of the processor by detecting and collecting the first, second, and third performance parameters, which can be performed in the human mind or by pen and paper.
to predict the first performance parameter based on the performance data: This limitation recites a mental process because it involves predicting/evaluating the first performance parameter of the processor from the performance data, which can be performed in the human mind or by pen and paper.
to adjust the first performance parameter based on the detected second and third performance parameters: This limitation recites a mental process because it involves evaluating the detected second and third performance parameters and adjusting the first performance parameter, which can be performed in the human mind or by pen and paper.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
[a]n energy consumption control computer program product, which is embodied on a non-transitory computer-readable storage medium and loaded and executed by a processor, comprising: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
a data collection programming module configured to…: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
a dual-model machine learning model programming module configured to execute a dual-model machine learning model: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
and an automatic control programming module configured to implement a fuzzy feedback control mechanism: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
[a]n energy consumption control computer program product, which is embodied on a non-transitory computer-readable storage medium and loaded and executed by a processor, comprising: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
a data collection programming module configured to…: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
a dual-model machine learning model programming module configured to execute a dual-model machine learning model: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
and an automatic control programming module configured to implement a fuzzy feedback control mechanism: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 10 recites similar limitations to claims 2 and 3. Therefore, claim 10 is rejected using the same rationale as claims 2 and 3.
Claim 11
Step 1: An article of manufacture, as above.
Step2A Prong 1: This claim does not recite an additional abstract idea, but the claim depends on claim 10 that depends on claim 9, which recites an abstract idea.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
the dual-model machine learning model is configured to integrate the first and second sub-machine learning models into a single machine learning model: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
the dual-model machine learning model is configured to integrate the first and second sub-machine learning models into a single machine learning model: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 12 recites similar limitations to claim 3. Therefore, claim 12 is rejected using the same rationale as claim 3.
Claim 13
Step 1: The claim recites a system; therefore, it is directed to the statutory category of a machine.
Step2A Prong 1: The claim recites, inter alia:
continuously detecting and collecting a performance data … wherein the performance data comprises a first performance parameter, a second performance parameter, and a third performance parameter: This limitation recites a mental process because it involves observing the performance data of the processor by detecting and collecting the first, second, and third performance parameters, which can be performed in the human mind or by pen and paper.
…to predict the first performance parameter based on the performance data: This limitation recites a mental process because it involves predicting/evaluating the first performance parameter of the processor from the performance data, which can be performed in the human mind or by pen and paper.
…to adjust the first performance parameter based on the detected second and third performance parameters: This limitation recites a mental process because it involves evaluating the detected second and third performance parameters and adjusting the first performance parameter, which can be performed in the human mind or by pen and paper.
Step2A Prong 2: This judicial exception is not integrated into a practical application because the
additional elements are as follows:
[a]n energy consumption control system, comprising: an electronic device comprising a processor which is configured to: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
execute a dual-model machine learning model: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
implement a fuzzy feedback control mechanism: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).
Step 2B: The claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception because the additional elements are as follows:
[a]n energy consumption control system, comprising: an electronic device comprising a processor which is configured to: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
execute a dual-model machine learning model: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
implement a fuzzy feedback control mechanism: Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea and cannot provide inventive concept (MPEP 2106.05(f)).
Even when considered in combination, these additional elements represent mere instructions to apply
an exception and therefore do not provide an inventive concept. The claim is ineligible.
Claim 14 recites similar limitations to claims 2 and 3. Therefore, claim 14 is rejected using the same rationale as claims 2 and 3.
Claim 15 recites similar limitations to claim 3. Therefore, claim 15 is rejected using the same rationale as claim 3.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 CFR 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, 7-9, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Wyatt (US 8700925 B2) in view of Bashir ("Short term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN", 2022).
Regarding claim 1,
Wyatt teaches [a]n energy consumption control method, comprising: performing following steps by a processor (Col. 14 Claim 1 of Wyatt, “A system comprising: a central processing unit (CPU); and a graphics processing unit (GPU) coupled to the CPU; wherein power used by the CPU and power used by the GPU are regulated in tandem using a fuzzy logic control system operable for implementing a plurality of fuzzy logic rules”, Col. 5 Lines 40-42, “The GPU 204 incorporates an on-chip system management unit (SMU) 206 that can be implemented in hardware, as a microcontroller”, Col. 6 Lines 50-52, “the thermal management system 130 runs in the kernel mode driver 222 and/or the on-chip SMU 206”
Wyatt manages CPU/GPU power consumption as a closed-loop method executed by an on-chip management unit and/or kernel-mode driver, which is the processor performing the steps.)
continuously detecting and collecting a performance data of the processor, wherein the performance data comprises a first performance parameter, a second performance parameter, and a third performance parameter (Col. 6 Lines 54-56, "The thermal management system 130 collects data from the sensors 208 and 210 that are connected to the SMU 206 via the I2C bus 212", Col. 6 Lines 1-12, “The SMU 206 can then compute an integrated value of power over a flexible time interval (e.g., a sliding window of time), unless this functionality is provided by the sensors themselves… Although not shown in FIG. 2, the system 200 can include other sensors, particularly temperature sensors. The temperature sensors can be situated to measure the temperatures of the CPU 202 and GPU 204, other internal components, and the skin (surface) temperature of the device incorporating the system 200.”, Col. 7 Lines 27-27, "CPU power and GPU power are measured over time using the power sensors 208 and 210", Col. 10 Lines 42-50, "Examples of input values for the control system 500 include, but are not limited to: … measured CPU and GPU power levels over sliding windows of time; … temperature values; … and CPU and GPU loading and percent utilization"
Wyatt continuously samples processor performance data over sliding time windows using sensors. The measured power corresponds to the first performance parameter, and the loading/utilization and temperature values correspond to the second and third performance parameters.)
implementing a fuzzy feedback control mechanism to adjust the first performance parameter based on the detected second and third performance parameters (Col. 2 Lines 54-64 of Wyatt, "the inventive thermal management system uses fuzzy logic control… a fuzzy logic control system is more likely to produce the proper response even with less accurate sensor data or little or no calibration.", Col. 8 Lines 52-56, "by measuring and monitoring the total integrated power and detecting when it exceeds the budget, it is possible to predict further thermal excursions and to adjust the CPU state and/or the GPU state accordingly", Col. 11 Lines 45-50, "The centroid defuzzification block 512 maps the range of solutions to a crisp output (control) setting 514 … a throttle adjustment such as reduce (or increase) the CPU and/or the GPU p-state by one or more levels", Col. 10 Lines 42-50, "Examples of input values for the control system 500 include, but are not limited to: … measured CPU and GPU power levels over sliding windows of time; … temperature values; … and CPU and GPU loading and percent utilization", Col. 13 Lines 5-9, “the control system 500 (FIG. 5) is triggered to use the new power sensor data, as well as other current data and information, to evaluate the fuzzy logic rules and to determine and implement a control action, as needed.”
Wyatt's fuzzy logic control system takes the measured loading/utilization and temperature as inputs and outputs a feedback adjustment to the CPU/GPU power state. Adjusting the performance state (power) based on the loading and temperature inputs to the control system corresponds to adjusting the first performance parameter based on the detected second and third performance parameters via a fuzzy feedback control mechanism.)
Wyatt does not teach executing a dual-model machine learning model to predict the first performance parameter based on the performance data.
Bashir, in the same field of endeavor, teaches executing a dual-model machine learning model to predict the first performance parameter based on the performance data (Pages 1 and 2 Introduction of Bashir, “Electrical load forecasting is prediction of electrical load using load history, weather information etc… These hybrids models have the ability of predicting load accurately by reducing AI complexity and increasing conventional methods accuracy by appending the advantages of both models while overcoming each other’s limitations. However, hybrid deep learning models (like CNN-LSTM) for the large and complex data sets operating on low configuration system not only suffers appropriate hyperparameter tuning but also results in slow computation speed… Therefore, in order to mitigate aforementioned issues for accurate load forecasting, this study proposes integrated prophet LSTM optimized by BP… Finally, the proposed novel hybrid Prophet-LSTM optimized by BPNN approach will be introduced.”, Page 4 Section 2.4.3 of Bashir, "The residual from the optimal model is considered to have non-linear tendency, thus are fed to LSTM model for training to improve the forecast."
Bashir predicts electrical load (i.e., power consumption over time) using a two-model hybrid of a Prophet model and an LSTM model fused into one forecaster. The Prophet and LSTM hybrid model approach corresponds to the claimed dual-model machine learning model, and the forecasted load corresponds to the predicted first performance parameter power.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Wyatt's continuous collection of processor power data with Bashir's dual Prophet-LSTM predictor in order to more accurately forecast both the long-term usage cycles and the short-term bursts of the power parameter that Wyatt's controller acts upon, thereby reducing prediction error and improving energy savings (Introduction of Bashir).
Regarding claim 7,
Wyatt teaches wherein the first performance parameter comprises one of a power, a thermal design power, and a combination thereof, and the second and third performance parameters comprise one of a load (Col. 2 Lines 66-67 and Col. 3 Line 1 of Wyatt, "the thermal solution can be reduced to less than the sum of the individual thermal design power or point (TDP) of those components", Col. 7 Lines 27-28, "CPU power and GPU power are measured over time using the power sensors 208 and 210", Col. 9 Lines 55-57, "The percent utilization of the CPU and the GPU can also be measured; thus, it is possible to determine how heavily loaded the CPU and GPU are", Col. 10 Lines 42-51, "Examples of input values for the control system 500 include, but are not limited to: … measured CPU and GPU power levels over sliding windows of time; … temperature values; … and CPU and GPU loading and percent utilization"
Wyatt's measured CPU/GPU power and thermal design power (TDP) corresponds to the first performance parameter, and Wyatt's measured loading/percent utilization corresponds to the load of the second and third performance parameters.)
Regarding claim 8,
Wyatt teaches wherein the performance data consists of time series of the first, second, and third performance parameters (Col. 10 Lines 42-51, "Examples of input values for the control system 500 include, but are not limited to: … measured CPU and GPU power levels over sliding windows of time; … temperature values; … and CPU and GPU loading and percent utilization", Col. 7 Lines 27-28, "CPU power and GPU power are measured over time using the power sensors", Col. 7 Lines 58-62, "used to determine a moving average and a rate of change … for future time periods."
Wyatt continuously samples processor performance data over sliding time windows using sensors. The measured power corresponds to the first performance parameter, and the loading/utilization and temperature values correspond to the second and third performance parameters. Wyatt's power/temperature/utilization data are collected over time as time series.)
Regarding claim 9,
Wyatt teaches [a]n energy consumption control computer program product, which is embodied on a non-transitory computer-readable storage medium and loaded and executed by a processor, comprising: (Col. 4 Lines 50-57 of Wyatt, "Computer storage media includes … random access memory (RAM), read only memory (ROM), electrically erasable programmable ROM (EEPROM), flash memory or other memory technology", Col. 10 Lines 33-37, "The fuzzy logic control system 500 can be implemented as part of the thermal management system 130 … as computer-executable instructions residing on some form of computer-usable medium"
Wyatt implements the fuzzy control system as computer-executable instructions on a computer storage medium executed by the management unit, corresponding to the computer program product on a non-transitory medium executed by a processor.)
a data collection programming module configured to continuously detect and collect a performance data of the processor, wherein the performance data comprises a first performance parameter, a second performance parameter, and a third performance parameter (Col. 6 Lines 54-56, "The thermal management system 130 collects data from the sensors 208 and 210 that are connected to the SMU 206 via the I2C bus 212", Col. 6 Lines 1-12, “The SMU 206 can then compute an integrated value of power over a flexible time interval (e.g., a sliding window of time), unless this functionality is provided by the sensors themselves… Although not shown in FIG. 2, the system 200 can include other sensors, particularly temperature sensors. The temperature sensors can be situated to measure the temperatures of the CPU 202 and GPU 204, other internal components, and the skin (surface) temperature of the device incorporating the system 200.”, Col. 7 Lines 27-27, "CPU power and GPU power are measured over time using the power sensors 208 and 210", Col. 10 Lines 42-51, "Examples of input values for the control system 500 include, but are not limited to: … measured CPU and GPU power levels over sliding windows of time; … temperature values; … and CPU and GPU loading and percent utilization"
Wyatt's data-collection functionality continuously gathers power (first parameter), loading/utilization (second parameter), and temperature (third parameter), corresponding to the claimed data collection programming module.)
an automatic control programming module configured to implement a fuzzy feedback control mechanism to adjust the first performance parameter based on the detected second and third performance parameters (Col. 2 Lines 54-64 of Wyatt, "the inventive thermal management system uses fuzzy logic control… a fuzzy logic control system is more likely to produce the proper response even with less accurate sensor data or little or no calibration.", Col. 8 Lines 52-56, "by measuring and monitoring the total integrated power and detecting when it exceeds the budget, it is possible to predict further thermal excursions and to adjust the CPU state and/or the GPU state accordingly", Col. 11 Lines 45-50, "The centroid defuzzification block 512 maps the range of solutions to a crisp output (control) setting 514 … a throttle adjustment such as reduce (or increase) the CPU and/or the GPU p-state by one or more levels"
Wyatt's fuzzy logic control system automatically adjusts the CPU/GPU power state based on detected loading and temperature, corresponding to the claimed automatic control programming module implementing a fuzzy feedback control mechanism.)
Wyatt does not teach a dual-model machine learning model programming module configured to execute a dual-model machine learning model to predict the first performance parameter based on the performance data.
Bashir, in the same field of endeavor, teaches a dual-model machine learning model programming module configured to execute a dual-model machine learning model to predict the first performance parameter based on the performance data (Pages 1 and 2 Introduction of Bashir, “Electrical load forecasting is prediction of electrical load using load history, weather information etc… These hybrids models have the ability of predicting load accurately by reducing AI complexity and increasing conventional methods accuracy by appending the advantages of both models while overcoming each other’s limitations. However, hybrid deep learning models (like CNN-LSTM) for the large and complex data sets operating on low configuration system not only suffers appropriate hyperparameter tuning but also results in slow computation speed… Therefore, in order to mitigate aforementioned issues for accurate load forecasting, this study proposes integrated prophet LSTM optimized by BP… Finally, the proposed novel hybrid Prophet-LSTM optimized by BPNN approach will be introduced.”, Page 4 Section 2.4.3 of Bashir, "The residual from the optimal model is considered to have non-linear tendency, thus are fed to LSTM model for training to improve the forecast."
Bashir's hybrid Prophet-LSTM predictor is the module that executes the dual-model machine learning model to predict the load (first performance parameter).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Wyatt's continuous collection of processor power data with Bashir's dual Prophet-LSTM predictor in order to more accurately forecast both the long-term usage cycles and the short-term bursts of the power parameter that Wyatt's controller acts upon, thereby reducing prediction error and improving energy savings (Introduction of Bashir).
Regarding claim 13,
Wyatt teaches [a]n energy consumption control system, comprising: an electronic device comprising a processor which is configured to: (Col. 5 Lines 35-37 of Wyatt, "The system 200 may be implemented as part of a computer system, such as but not limited to a notebook or nettop type of device", Col. 5 Lines 38-41, "The system 200 includes a CPU 202 and a GPU 204 … The GPU 204 incorporates an on-chip system management unit (SMU) 206"
Wyatt's computer system with a CPU/GPU and on-chip management unit corresponds to the electronic device comprising a processor.)
continuously detect and collect a performance data of the processor, wherein the performance data comprises a first performance parameter, a second performance parameter, and a third performance parameter (Col. 6 Lines 54-56, "The thermal management system 130 collects data from the sensors 208 and 210 that are connected to the SMU 206 via the I2C bus 212", Col. 6 Lines 1-12, “The SMU 206 can then compute an integrated value of power over a flexible time interval (e.g., a sliding window of time), unless this functionality is provided by the sensors themselves… Although not shown in FIG. 2, the system 200 can include other sensors, particularly temperature sensors. The temperature sensors can be situated to measure the temperatures of the CPU 202 and GPU 204, other internal components, and the skin (surface) temperature of the device incorporating the system 200.”, Col. 7 Lines 27-27, "CPU power and GPU power are measured over time using the power sensors 208 and 210", Col. 10 Lines 42-51, "Examples of input values for the control system 500 include, but are not limited to: … measured CPU and GPU power levels over sliding windows of time; … temperature values; … and CPU and GPU loading and percent utilization"
Wyatt continuously collects power, loading/utilization, and temperature as the first, second, and third performance parameters.)
implement a fuzzy feedback control mechanism to adjust the first performance parameter based on the detected second and third performance parameters (Col. 2 Lines 54-64 of Wyatt, "the inventive thermal management system uses fuzzy logic control… a fuzzy logic control system is more likely to produce the proper response even with less accurate sensor data or little or no calibration.", Col. 8 Lines 52-56, "by measuring and monitoring the total integrated power and detecting when it exceeds the budget, it is possible to predict further thermal excursions and to adjust the CPU state and/or the GPU state accordingly", Col. 11 Lines 45-50, "The centroid defuzzification block 512 maps the range of solutions to a crisp output (control) setting 514 … a throttle adjustment such as reduce (or increase) the CPU and/or the GPU p-state by one or more levels"
Wyatt's fuzzy logic control system takes the measured loading/utilization and temperature as inputs and outputs a feedback adjustment to the CPU/GPU power state. Adjusting the performance state (power) based on the detected loading and temperature corresponds to adjusting the first performance parameter based on the detected second and third performance parameters via a fuzzy feedback control mechanism.)
Wyatt does not teach executing a dual-model machine learning model to predict the first performance parameter based on the performance data.
Bashir, in the same field of endeavor, teaches execute a dual-model machine learning model to predict the first performance parameter based on the performance data (Pages 1 and 2 Introduction of Bashir, “Electrical load forecasting is prediction of electrical load using load history, weather information etc… These hybrids models have the ability of predicting load accurately by reducing AI complexity and increasing conventional methods accuracy by appending the advantages of both models while overcoming each other’s limitations. However, hybrid deep learning models (like CNN-LSTM) for the large and complex data sets operating on low configuration system not only suffers appropriate hyperparameter tuning but also results in slow computation speed… Therefore, in order to mitigate aforementioned issues for accurate load forecasting, this study proposes integrated prophet LSTM optimized by BP… Finally, the proposed novel hybrid Prophet-LSTM optimized by BPNN approach will be introduced.”, Page 4 Section 2.4.3 of Bashir, "The residual from the optimal model is considered to have non-linear tendency, thus are fed to LSTM model for training to improve the forecast."
Bashir predicts electrical load (i.e., power consumption over time) using a two-model hybrid of a Prophet model and an LSTM model fused into one forecaster. The Prophet and LSTM hybrid model approach corresponds to the claimed dual-model machine learning model, and the forecasted load corresponds to the predicted first performance parameter power.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Wyatt's continuous collection of processor power data with Bashir's dual Prophet-LSTM predictor in order to more accurately forecast both the long-term usage cycles and the short-term bursts of the power parameter that Wyatt's controller acts upon, thereby reducing prediction error and improving energy savings (Introduction of Bashir).
Claims 2-5, 10, 12, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Wyatt (US 8700925 B2) in view of Bashir ("Short term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN", 2022), and in further view of Ahuja (US 10234833 B2).
Regarding claim 2,
Wyatt does not teach selectively executing a first sub-machine learning model comprised in the dual-model machine learning model to predict a first period first performance parameter in a first period; selectively executing a second sub-machine learning model comprised in the dual-model machine learning model to predict a second period first performance parameter in a second period; and selectively correcting the first period first performance parameter based on the second period first performance parameter to use as the first performance parameter
Bashir, in the same field of endeavor, teaches selectively executing a first sub-machine learning model comprised in the dual-model machine learning model to predict a first period first performance parameter in a first period (Page 3 Section 2.3 of Bashir, "The Prophet forecasting method considers electrical load data complicated features such as trend, seasonality and holidays", Pages 1 and 2 Introduction, “Electrical load forecasting is prediction of electrical load using load history, weather information etc. Load forecasting. Load forecasting is generally categorized in three types: short term (few hours to few weeks), mid-term (week to year) and long term (more than a year)... Therefore, in order to mitigate aforementioned issues for accurate load forecasting, this study proposes integrated prophet LSTM optimized by BP… Finally, the proposed novel hybrid Prophet-LSTM optimized by BPNN approach will be introduced.”
Bashir teaches a dual-model hybrid forecasting system with a first sub-model (Prophet) that is selectively executed to predict electrical load, which is the first performance parameter. Prophet models the trend, seasonality, and holiday features of the load data and outputs predicted load values for a short-term forecast period such as 24 hours, one week, or one month. This corresponds to executing a first sub-machine learning model in a dual-model system to predict a first performance parameter in a first period.)
selectively executing a second sub-machine learning model comprised in the dual-model machine learning model to predict a … period first performance parameter in a … period (Page 3 Section 2.3 of Bashir, "The Prophet forecasting method considers electrical load data complicated features such as trend, seasonality and holidays", Page 3 Section 2.2 of Bashir, "LSTM is special type of the RNN, which is widely applied due to its extraordinary ability to retain and memorize long sequences of electrical load data", Page 4 Section 2.4.3 of Bashir, "these non-linear components of the load data are given to LSTM model."
Bashir teaches a dual-model hybrid forecasting system in which the second sub-model (LSTM) is selectively executed to predict the same first performance parameter (electrical load) for the same forecast period as the Prophet model. The LSTM predicts the residual/non-linear part of the electrical load while the Prophet model predicts the structural/seasonal part of the electrical load. The LSTM output is then combined with the Prophet output to produce the final predicted load for that period.)
selectively correcting the first period first performance parameter… to use as the first performance parameter (Page 5 Section 2.4.4 of Bashir, "The functionality of BPNN here in our hybrid model is to optimize the forecast from optimal model and residual of optimal model forecasted by the deep learning LSTM model", Page 1 Abstract of Bashir, "both the forecasted data from Prophet and LSTM are trained by Back Propagation Neural Network (BPNN)"
Bashir teaches that the BPNN optimizes the final forecast by combining the Prophet output with the LSTM residual output. The BPNN corrects the Prophet prediction using the LSTM-predicted residual for the same forecast period, producing a final optimized load value.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Wyatt's continuous collection of processor power data with Bashir's dual Prophet-LSTM predictor in order to more accurately forecast both the long-term usage cycles and the short-term bursts of the power parameter that Wyatt's controller acts upon, thereby reducing prediction error and improving energy savings (Introduction of Bashir).
Wyatt in view of Bashir does not appear to explicitly teach selectively executing a … machine learning model to predict a second period first performance parameter in a second period.
Ahuja, in the same field of endeavor, teaches selectively executing a … machine learning model to predict a second period first performance parameter in a second period (Col. 16 Lines 64-66 of Ahuja, "the power usage predictor 1616 performs a linear regression using the sensor data from a relatively short recent time (such as less than the past 15, 10, or 5 minutes)", Col. 18 Lines 43-47, “ the data center manager 1208 may predict the power usage based on the sensor data sample with a different machine-learning-based algorithm, such as by directly predicting the power usage based on a neural network.”, Col. 24 Claim 5, "ahueriod of time", Col. 24 Claim 6, "based on the power usage over the previous fifteen minutes"
Ahuja predicts future processor/platform power using a machine-learning-based scheme that operates over a longer first period and a shorter second period. Ahuja's shorter, second period corresponds to the claimed second period, and the power predicted therein corresponds to the second-period first performance parameter.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Wyatt in view of Bashir's dual Prophet-LSTM power predictor with Ahuja's prediction over a distinct shorter second period in order to capture short-term power variations separately from long-term usage cycles and improve the prediction accuracy of the model (Col. 10 Lines 18-28 of Ahuja).
Regarding claim 3,
Wyatt teaches implementing the fuzzy feedback control mechanism to add a fine-tuning value to the first performance parameter based on the detected second and third performance parameters to adjust the first performance parameter (Col. 11 h. of Wyatt, "If (Over Shared Budget) and ((CPU temperature > High) or (Integral Burden is Over)) Reduce CPU Limit by 1 Level", Col. 11 Lines 47-50, "reduce (or increase) the CPU and/or the GPU p-state by one or more levels"
Wyatt's fuzzy controller adjusts the power state incrementally by discrete levels based on the detected temperature and integral-burden inputs. Adding or subtracting a discrete level to the power state corresponds to adding a fine-tuning value to the first performance parameter based on the detected second and third performance parameters.)
Wyatt does not teach selectively executing the dual-model machine learning model to predict a first period first performance parameter in a first period and a second period first performance parameter in a second period and to correct the first period first performance parameter based on the second period first performance parameter to use as the first performance parameter, wherein the first period and the second period have different durations.
Bashir, in the same field of endeavor, teaches selectively executing the dual-model machine learning model to predict a first period first performance parameter in a first period … and to correct the first period first performance parameter based on the second period first performance parameter to use as the first performance parameter (Page 4 Section 2.4.3 of Bashir, "The residual from the optimal model is considered to have non-linear tendency, thus are fed to LSTM model for training to improve the forecast.", Page 5 Section 3.1 of Bashir, "The combination of proposed hybrid models can forecast load over different time horizons such as 24 h, one week, and one month at 15-minutes time interval."
Bashir's dual Prophet-LSTM model predicts the electrical load over a first (longer-term) period via the Prophet sub-model and corrects that forecast using the LSTM-predicted residual through the BPNN, corresponding to predicting the first-period first performance parameter and correcting it to produce the first performance parameter.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Wyatt's continuous collection of processor power data with Bashir's dual Prophet-LSTM predictor in order to more accurately forecast both the long-term usage cycles and the short-term bursts of the power parameter that Wyatt's controller acts upon, thereby reducing prediction error and improving energy savings (Introduction of Bashir).
Wyatt in view of Bashir does not appear to explicitly teach predicting a second period first performance parameter in a second period … wherein the first period and the second period have different durations.
Ahuja, in the same field of endeavor, teaches predicting a second period first performance parameter in a second period … wherein the first period and the second period have different durations (Col. 16 Lines 64-66 of Ahuja, "the power usage predictor 1616 performs a linear regression using the sensor data from a relatively short recent time (such as less than the past 15, 10, or 5 minutes)", Col. 24 Claim 5, "the future power usage based on the power usage of a second period of time associated with the second class and shorter than the first period of time", Col. 24 Claim 6, "predicting the future power usage based on the power usage over the previous hour and … based on the power usage over the previous fifteen minutes"
Ahuja predicts future power over a longer first period and a distinct shorter second period. The shorter second period corresponds to the claimed second period, and the second period being shorter than the first corresponds to the first and second periods having different durations.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Wyatt in view of Bashir's dual Prophet-LSTM power predictor with Ahuja's prediction over a distinct shorter second period of a different duration in order to capture short-term power variations separately from long-term usage cycles and improve the prediction accuracy of the model (Col. 10 Lines 18-28 of Ahuja).
Regarding claim 4,
Wyatt does not teach wherein the first sub-machine learning model comprises one of a neural network model, a deep neural network model, a convolutional neural network model, a multilayer perceptron model, a moving average model, an exponential smoothing model, an autoregressive model, a vector autoregressive model, an autoregressive moving average model, an integrated moving average autoregressive model, a regression tree model, a growth model, a latent growth curve model, a latent growth model, a Fourier model, a Fourier series model, a trend model, a prophet model, and a combination thereof.
Bashir, in the same field of endeavor, teaches wherein the first sub-machine learning model comprises one of a neural network model, a deep neural network model, a convolutional neural network model, a multilayer perceptron model, a moving average model, an exponential smoothing model, an autoregressive model, a vector autoregressive model, an autoregressive moving average model, an integrated moving average autoregressive model, a regression tree model, a growth model, a latent growth curve model, a latent growth model, a Fourier model, a Fourier series model, a trend model, a prophet model, and a combination thereof (Page 3 Section 2.3 of Bashir, "The Prophet forecasting method considers electrical load data complicated features such as trend, seasonality and holidays… the seasonality function in the prophet model can be modeled by using Fourier series"
Bashir's first sub-model is a Prophet model, which is expressly one of the recited alternatives.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Wyatt's continuous collection of processor power data with Bashir's dual Prophet-LSTM predictor in order to more accurately forecast both the long-term usage cycles and the short-term bursts of the power parameter that Wyatt's controller acts upon, thereby reducing prediction error and improving energy savings (Introduction of Bashir).
Regarding claim 5,
Wyatt does not teach wherein the second sub-machine learning model comprises one of a neural network model, a deep neural network model, a convolutional neural network model, a recurrent neural network model, a gated recurrent unit model, a long short-term memory model, a multilayer perceptron model, and a combination thereof
Bashir, in the same field of endeavor, teaches wherein the second sub-machine learning model comprises one of a neural network model, a deep neural network model, a convolutional neural network model, a recurrent neural network model, a gated recurrent unit model, a long short-term memory model, a multilayer perceptron model, and a combination thereof (Page 3 Section 2.2 of Bashir, "LSTM is special type of the RNN", Page 1 Abstract, "these residuals (non-linear data) are trained by employing LSTM"
Bashir's second sub-model is a long short-term memory (LSTM) model, which is one of the recited alternatives.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Wyatt's continuous collection of processor power data with Bashir's dual Prophet-LSTM predictor in order to more accurately forecast both the long-term usage cycles and the short-term bursts of the power parameter that Wyatt's controller acts upon, thereby reducing prediction error and improving energy savings (Introduction of Bashir).
Claim 10 recites similar limitations to claims 2 and 3. Therefore, claim 10 is rejected using the same rationale as claims 2 and 3.
Claim 12 recites similar limitations to claim 3. Therefore, claim 12 is rejected using the same rationale as claim 3.
Claim 14 recites similar limitations to claims 2 and 3. Therefore, claim 14 is rejected using the same rationale as claims 2 and 3.
Claim 15 recites similar limitations to claim 3. Therefore, claim 15 is rejected using the same rationale as claim 3.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Wyatt (US 8700925 B2) in view of Bashir ("Short term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN", 2022), in view of Ahuja (US 10234833 B2), and in further view of Sourov (US 20240119928 A1).
Regarding claim 6,
Wyatt teaches the fine-tuning value has a numerical value in a range between [one to two levels] of a value of the first performance parameter (Col. 10 a. and Col. 11 j., “If (Skin Temperature .gtoreq. Very High) Reduce GPU Limit by 1 Level Reduce CPU Limit by 1 Level… If (Very Over Shared Budget) and (Integral Burden is Very Over) Reduce CPU Limit by 2 Levels Reduce GPU Limit by 1 Level”
Wyatt's fuzzy feedback controller adjusts the first performance parameter (power/p-state) by a bounded fine-tuning increment.).
Wyatt does not teach the … value has a numerical value in a range between ±2% of a value of the … parameter.
However, analogous art Sourov teaches the … value has a numerical value in a range between ±2% of a value of the … parameter (Paragraph 132 of Sourov, “all measurements, values, ratings, positions, magnitudes, sizes, and other specifications that are set forth in this specification, including in the claims that follow, are approximate, not exact. Such amounts are intended to have a reasonable range that is consistent with the functions to which they relate and with what is customary in the art to which they pertain. For example, unless expressly stated otherwise, a parameter value or the like may vary by as much as plus or minus ten percent from the stated amount or range.”
Sourov teaches that a parameter value may be expressed as a range defined as plus or minus ten percent of the stated amount. Paragraph 65 of the instant specification states that "[t]he fine-tuning value is preferably in a range between ±2% of the TDP prediction value," such that the recited ±2% is a preferred, non-critical value of the first performance parameter rather than a critical limitation. Since the claimed ±2% range lies within the broader ±10% range disclosed by Sourov, the prior art range renders the narrower claimed range prima facie obvious (MPEP 2131.03(II)).)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to modify the bounded level-based fine-tuning increment of Wyatt as a range of plus or minus a percentage of the value of the first performance parameter, as taught by Sourov, in order to define the fine-tuning increment as a proportion of the parameter being controlled to specify a bounded control adjustment (Paragraph 132 of Sourov).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Wyatt (US 8700925 B2) in view of Bashir ("Short term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN", 2022), in view of Ahuja (US 10234833 B2), and in further view of Lai ("Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks", 2018).
Regarding claim 11,
Wyatt in view of Bashir does not teach explicitly wherein the … machine learning model is configured to integrate the first and second sub-machine learning models into a single machine learning model.
Lai, in the same field of endeavor, teaches wherein the dual-model machine learning model is configured to integrate the first and second sub-machine learning models into a single machine learning model (Page 1 Abstract of Lai, "we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet)… LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends", Page 4 Section 3.6 of Lai, "The final prediction of LSTNet is then obtained by … integrating the outputs of the neural network part and the AR component", Page 4 Section 3.7 of Lai, "the corresponding optimization objective is formulated as, minimize Θ", Page 5 Section 3.8 of Lai, "The problem then becomes a regression task with a set of feature-value pairs {Xt ,Yt+h }, and can be solved by Stochastic Gradient Decent (SGD) or its variants such as Adam"
Lai’s short-term (CNN/recurrent) component and its long-term/linear (autoregressive) component are trained together under a single optimization objective by a single optimizer and produce one final prediction.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to combine Wyatt in view of Bashir's separate Prophet and LSTM sub-models with Lai's integration of long- and short-term sub-models into a single network in order to jointly optimize the long- and short-term predictions and improve robustness of the combined forecast (Section 5 of Lai).
Conclusion
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/M.M.H./Examiner, Art Unit 2125
/KAMRAN AFSHAR/Supervisory Patent Examiner, Art Unit 2125
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