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 .
Drawings
The drawings filed on 04/19/2024 are accepted.
Specification
The specification filed on 04/19/2024 is accepted.
Information Disclosure Statement
The examiner has considered the information disclosure statements (IDS) submitted on 02/05/2026.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “obtaining energy efficiency data of a target device within a preset time period”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “obtaining” in the context of this claim encompasses the user collects energy consumption information of an appliance.
The limitation of “determining a plurality of influencing factors from the energy efficiency data according to a preset energy efficiency indicator and a feature extraction algorithm”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “determining” in the context of this claim encompasses the user determines the factors that have the influence on the energy consumption of a certain appliance such as outside weather, peak hours, special events.
The limitation of “determining a regression prediction model according to the plurality of influencing factors and the preset energy efficiency indicator”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “determining” in the context of this claim encompasses the user generates a model/an equation using some specific factors.
The limitation of “generating a trend graph of energy consumption corresponding to the preset time period according to a first predicted value at each moment”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “generating” in the context of this claim encompasses the user creates a graph of energy consumption values of the appliance for each day of a month.
If a claim, under the broadest reasonable interpretation covers concepts that can be performed in the human mind, or by a human using a pen and paper, including observation, evaluation, judgment, or opinion, or if a claim recites a mathematical concept, it will be considered as falling within the "mental process and/or mathematical concept” grouping of abstract ideas.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites the additional element of “inputting the plurality of influencing factors into the regression prediction model and generating a first prediction value at each moment within the preset time period”. This limitation is recited at a high-level of generality (i.e., as a generic device performing the generic computer function) such that it amounts to no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The additional element of “inputting the plurality of influencing factors into the regression prediction model and generating a first prediction value at each moment within the preset time period” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Claim 2 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites the method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “obtaining historical energy efficiency data as training data”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “obtaining” in the context of this claim encompasses the user collects past data.
The limitation of “extracting a plurality of feature data from the training data according to the preset energy efficiency indicator and the feature extraction algorithm”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “extracting” in the context of this claim encompasses the user selects certain features from the collected data.
The limitation of “randomly inputting one or more feature data into a linear regression model, and constructing regression prediction sub-models corresponding to the one or more feature data” recites a mathematical concept.
The limitation of “calculating a score value of each of the regression prediction sub-models according to the second prediction value and the energy efficiency indicator”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “calculating” in the context of this claim encompasses the user assigns a score for each model based on some factors.
The limitation of “determining at least one regression prediction sub-model whose score value is greater than a preset threshold as the regression prediction model”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “determining” in the context of this claim encompasses the user compares the selecting a model based on the comparison.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites the additional element of “obtaining a second prediction value corresponding to the one or more feature data according to the one or more feature data and corresponding regression prediction sub-models”. This limitation is recited at a high-level of generality (i.e., as a generic device performing the generic computer function) such that it amounts to no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The additional element of “obtaining a second prediction value corresponding to the one or more feature data according to the one or more feature data and corresponding regression prediction sub-models” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Claim 3 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites the method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “in response that no regression prediction sub-model has a score value that is greater than the preset threshold, regaining updated training data”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “regaining” in the context of this claim encompasses the user changes/modifies the data based on some factors.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim recites the additional element of “retraining the linear regression model by using the updated training data, and obtaining an updated regression prediction sub-model”. This limitation is recited at a high-level of generality (i.e., as a generic device performing the generic computer function) such that it amounts to no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The additional element of “retraining the linear regression model by using the updated training data, and obtaining an updated regression prediction sub-model” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Claim 4 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites the method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “obtaining the updated training data by removing data that does not conform to a preset data type from the historical energy efficiency data”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “obtaining” in the context of this claim encompasses the user changes/modifies the data by deleting certain data.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Indeed, the claim does not recite any additional element besides the limitation that can be performed in a human mind. The claim is not patent eligible.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claim does not recite any additional element beside the limitation that can be performed in a human mind. The claim is not patent eligible.
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites the method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “determining a plurality of energy consumption factors related to the energy efficiency indicator”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “determining” in the context of this claim encompasses the user determines energy demand, load of an appliance during a peak window.
The limitation of “determining target data from the energy efficiency data according to the plurality of energy consumption factors and the feature extraction algorithm”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “determining” in the context of this claim encompasses the user determines a temperature setting for a heater or an HVAC during a certain time such as peak time.
The limitation of “obtaining correlation values between the target data and the plurality of energy consumption factors”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “obtaining” in the context of this claim encompasses the user measures the energy consumption value of the heater/HVAC based on the setting during the peak time.
The limitation of “determining the plurality of influencing factors from the target data according to a preset number and a preset sorting rule of the correlation values”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “determining” in the context of this claim encompasses the user determines the energy load, consumption based on the temperature setting.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Indeed, the claim does not recite any additional element besides the limitation that can be performed in a human mind. The claim is not patent eligible.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claim does not recite any additional element beside the limitation that can be performed in a human mind. The claim is not patent eligible.
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites the method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “in responses that a user's prediction request is received, determining at least one influencing factor from the plurality of influencing factors according to the prediction request”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “determining” in the context of this claim encompasses the user measures a current load from an appliance based on request.
The limitation of “determining a regression prediction sub-model comprising the at least one influencing factor as the regression prediction model according to the at least one influencing factor and the energy efficiency indicator”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “determining” in the context of this claim encompasses the user generates a model/an equation using some specific factors.
The limitation of “in responses that no user's prediction request is received, determining the regression prediction sub-model comprising the plurality of influencing factors as the regression prediction model according to the plurality of influencing factor and the energy efficiency indicator”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “determining” in the context of this claim encompasses the user generates a model/an equation using certain factors according to a situation.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Indeed, the claim does not recite any additional element besides the limitation that can be performed in a human mind. The claim is not patent eligible.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claim does not recite any additional element beside the limitation that can be performed in a human mind. The claim is not patent eligible.
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites the method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “obtaining production data of the target device during a production process”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “determining” in the context of this claim encompasses the user collects energy consumption data.
The limitation of “performing a first-order difference calculation on the production data, and obtaining the energy efficiency data” recites a mathematical concept.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Indeed, the claim does not recite any additional element besides the limitation that can be performed in a human mind. The claim is not patent eligible.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the claim does not recite any additional element beside the limitation that can be performed in a human mind. The claim is not patent eligible.
Claim 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites the method which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. The additional element of “wherein the trend graph of energy consumption comprises a curve formed by the first predicted value at each moment in the preset time period and a curve formed by the energy efficiency indicator” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not integrate into a practical application (see 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. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “wherein the trend graph of energy consumption comprises a curve formed by the first predicted value at each moment in the preset time period and a curve formed by the energy efficiency indicator” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitation that amounts to merely indicating a field of use or technological environment in which to apply a judicial exception does not amount to significantly more than the judicial exception (see MPEP 2106.05(h)).
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a device which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “obtain energy efficiency data of a target device within a preset time period”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “obtain” in the context of this claim encompasses the user collects energy consumption information of an appliance.
The limitation of “determine a plurality of influencing factors from the energy efficiency data according to a preset energy efficiency indicator and a feature extraction algorithm”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “determine” in the context of this claim encompasses the user determines the factors that have the influence on the energy consumption of a certain appliance such as outside weather, peak hours, special events.
The limitation of “determine a regression prediction model according to the plurality of influencing factors and the preset energy efficiency indicator”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “determine” in the context of this claim encompasses the user generates a model/an equation using some specific factors.
The limitation of “generate a trend graph of energy consumption corresponding to the preset time period according to a first predicted value at each moment”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “generate” in the context of this claim encompasses the user creates a graph of energy consumption values of the appliance for each day of a month.
If a claim, under the broadest reasonable interpretation covers concepts that can be performed in the human mind, or by a human using a pen and paper, including observation, evaluation, judgment, or opinion, or if a claim recites a mathematical concept, it will be considered as falling within the "mental process and/or mathematical concept” grouping of abstract ideas.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “An electronic device”, “a processor” and “a non-transitory storage medium”. The additional elements are recited at a high-level of generality (i.e., as a generic device performing the generic computer functions) such that they amount no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim recites the additional element of “input the plurality of influencing factors into the regression prediction model and generating a first prediction value at each moment within the preset time period”. This limitation is recited at a high-level of generality (i.e., as a generic device performing the generic computer function) such that it amounts to no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “An electronic device”, “a processor” and “a non-transitory storage medium” to perform the generic computer functions amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The additional element of “input the plurality of influencing factors into the regression prediction model and generating a first prediction value at each moment within the preset time period” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Claim 10 is rejected by the same reason as of claim 2, since these claims recite the similar limitations.
Claim 11 is rejected by the same reason as of claim 3, since these claims recite the similar limitations.
Claim 12 is rejected by the same reason as of claim 4, since these claims recite the similar limitations.
Claim 13 is rejected by the same reason as of claim 5, since these claims recite the similar limitations.
Claim 14 is rejected by the same reason as of claim 6, since these claims recite the similar limitations.
Claim 15 is rejected by the same reason as of claim 7, since these claims recite the similar limitations.
Claim 16 is rejected by the same reason as of claim 8, since these claims recite the similar limitations.
Claim 17 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1:
The claim recites a non-transitory storage medium which falls within at least one of the four statutory categories of patent eligible subject matter.
Step 2:
Step 2A (prong 1):
The limitation of “obtaining energy efficiency data of a target device within a preset time period”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “obtaining” in the context of this claim encompasses the user collects energy consumption information of an appliance.
The limitation of “determining a plurality of influencing factors from the energy efficiency data according to a preset energy efficiency indicator and a feature extraction algorithm”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “determining” in the context of this claim encompasses the user determines the factors that have the influence on the energy consumption of a certain appliance such as outside weather, peak hours, special events.
The limitation of “determining a regression prediction model according to the plurality of influencing factors and the preset energy efficiency indicator”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “determining” in the context of this claim encompasses the user generates a model/an equation using some specific factors.
The limitation of “generating a trend graph of energy consumption corresponding to the preset time period according to a first predicted value at each moment”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, “generating” in the context of this claim encompasses the user creates a graph of energy consumption values of the appliance for each day of a month.
If a claim, under the broadest reasonable interpretation covers concepts that can be performed in the human mind, or by a human using a pen and paper, including observation, evaluation, judgment, or opinion, or if a claim recites a mathematical concept, it will be considered as falling within the "mental process and/or mathematical concept” grouping of abstract ideas.
Step 2A (prong 2):
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “an electronic device”, “at least one processor” and “a non-transitory storage medium”. The additional elements are recited at a high-level of generality (i.e., as a generic device performing the generic computer functions) such that they amount no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim recites the additional element of “inputting the plurality of influencing factors into the regression prediction model and generating a first prediction value at each moment within the preset time period”. This limitation is recited at a high-level of generality (i.e., as a generic device performing the generic computer function) such that it amounts to no more than mere instructions to apply the exception using the generic computer components (MPEP 2106.05(f)). Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of ““an electronic device”, “at least one processor” and “a non-transitory storage medium” to perform the generic computer functions amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
The additional element of “inputting the plurality of influencing factors into the regression prediction model and generating a first prediction value at each moment within the preset time period” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Claim 18 is rejected by the same reason as of claim 2, since these claims recite the similar limitations.
Claim 19 is rejected by the same reason as of claim 3, since these claims recite the similar limitations.
Claim 20 is rejected by the same reason as of claim 4, since these claims recite the similar limitations.
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 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, 9 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. (US Pub. 2023/0239784) in view of Bharti et al. (US Pub. 2018/0329406).
As per claim 1, Zhang teaches a method for predicting energy consumption [abstract, “an energy-saving method … The method includes … predicting a load trend of the target cell according to the energy-consumption influencing factor data”], the method comprising:
obtaining energy efficiency data of a target device within a preset time period [abstract, “collecting energy-consumption influencing factor data”; paragraph 0035, “energy-consumption influencing factor data corresponding to each time granularity (for example, each hour) of the target cell in a past period of time (for example, one month) may be collected”; paragraph 0041, “the energy-consumption influencing factor data includes at least one of: load information of the target cell”];
determining a plurality of influencing factors from the energy efficiency data according to a preset energy efficiency indicator and a feature extraction algorithm [paragraph 0035, “energy-consumption influencing factor data corresponding to each time granularity (for example, each hour) of the target cell in a past period of time (for example, one month) may be collected”; paragraph 0032, “The energy-consumption influencing factor data refers to data that has an influence on the energy consumption of the target cell”; paragraphs 0041-0044, “the energy-consumption influencing factor data includes a variety of load information … the geographical location of the target cell … the information about the non-network factor such as weather, time lists of special events, road traffic congestion, special holidays …”; paragraphs 0055-0058, “the energy-consumption influencing factor data is coded, which facilitates the extraction of load characteristics and the prediction of the load trend through modeling … the load characteristic (mainly referring to a time series characteristic of a load as a function of time) is extracted … The load characteristic includes, but is not limited to, a trend characteristic as a function of time and a periodic characteristic. For example, in a specific time period, the load continues to rise or fall smoothly, rises or falls rapidly, fluctuates within a certain range, rises or falls regularly according to a certain period, or the like”; Table 1 shows indicator of weather data and the coding of the weather data in the energy-consumption influencing factor data];
determining a regression prediction model according to the plurality of influencing factors and the preset energy efficiency indicator [Fig. 2, paragraphs 0051-0060, “energy-consumption influencing factor data of a target cell is collected … The energy-consumption influencing factor data includes data in a past period of time (for example, six months), and a collection time granularity may be one hour … the energy-consumption influencing factor data is coded, which facilitates the extraction of load characteristics and the prediction of the load trend through modeling … the load characteristic (mainly referring to a time series characteristic of a load as a function of time) is extracted … The load characteristic includes, but is not limited to, a trend characteristic as a function of time and a periodic characteristic. For example, in a specific time period, the load continues to rise or fall smoothly, rises or falls rapidly, fluctuates within a certain range, rises or falls regularly according to a certain period, or the like … and a model is made according to the load characteristic … a machine learning prediction model is established according to the load characteristic”; paragraph 0072, “the machine learning prediction model is established through a regression modeling algorithm”; Tables 1-2 show indicator of weather data and the coding of the weather data in the energy-consumption influencing factor data, and the mapping of load characteristics and prediction models];
inputting the plurality of influencing factors into the regression prediction model and generating a first prediction value at each moment within the preset time period [paragraph 0064, “load factor values of the target cell at different time points in a future period of time are predicted through the machine learning prediction model, the amount of loads is predicted according to the load factor values”];
generating a trend of energy consumption corresponding to the preset time period according to a first predicted value at each moment [paragraph 0035, “the load trend of the target cell in the future may be predicted through a machine learning prediction model or the like. For example, a load factor value of each time granularity (for example, each hour) of the target cell in a future period of time (for example, one week) is predicted according to periodic characteristics and development trend characteristics (for example, data about a factor continues to rise or fall smoothly, rises or falls rapidly, fluctuates within a certain range, rises or falls regularly according to a certain period, or the like) of the energy-consumption influencing factor data”].
Zhang does not teach
generating a trend graph of energy consumption corresponding to the preset time period according to a first predicted value at each moment (emphasis added).
Bharti teaches
generating a trend graph of energy consumption corresponding to the preset time period according to a first predicted value at each moment [paragraph 0047, “FIG. 4 depicts an example graph illustrating trends, by-month, in energy consumed by an electrical device across multiple (5) years. Lines indicated by reference numerals 402-424 are paired with the twelve months of the year as follows: 402—January; 404—February; 406—March; 408—April; 410—May; 412—June; 414—July; 416—August; 418—September; 420—October; 422—November; 424—December”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for energy saving of Zhang to include a process of generating a trend graph of energy consumption corresponding to the preset time period according to a first predicted value at each moment of Bharti. Doing so would help observing the change of energy consumption of a device over a period of time to control the load of the device at a certain time window.
Claim 9 is rejected by the same reason as of claim 1, since these claims recite the similar limitations.
Zhang further teaches
a processor [paragraph 0008, “one or more processors”]; and
a non-transitory storage medium, coupled to the processor, that stores a plurality of instructions, which cause the processor to [paragraph 0008, “one or more processors and a storage apparatus that is configured to store one or more programs. When executed by the one or more processors, the one or more programs cause the one or more processors to implement the preceding energy-saving method]:
Claim 17 is rejected by the same reason as of claim 9, since these claims recite the similar limitations.
Claims 2, 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. in view of Bharti et al. in view of Jiang et al. (US Patent 8,738,334) and further in view of Matsuoka et al. (US Pub. 2015/0167989).
As per claim 2, Zhang and Bharti teach the method for predicting energy consumption according to claim 1.
Zhang further teaches
obtaining historical energy efficiency data as training data [paragraph 0039, “the load trend of the target cell in the future may be predicted based on historical load information of the target cell”; paragraph –02, Table 2, disclose load characteristics including historical load are used to train a certain prediction model; paragraph 0071, “After the prediction model is obtained, load trends of multiple time granularities in the future may be predicted according to historical energy-consumption influencing factor data”];
extracting a plurality of feature data from the training data according to the preset energy efficiency indicator and the feature extraction algorithm [paragraphs 0055-0058, “the energy-consumption influencing factor data is coded, which facilitates the extraction of load characteristics and the prediction of the load trend through modeling … the load characteristic (mainly referring to a time series characteristic of a load as a function of time) is extracted … The load characteristic includes, but is not limited to, a trend characteristic as a function of time and a periodic characteristic. For example, in a specific time period, the load continues to rise or fall smoothly, rises or falls rapidly, fluctuates within a certain range, rises or falls regularly according to a certain period, or the like”; Table 1 shows indicator of weather data and the coding of the weather data in the energy-consumption influencing factor data];
constructing regression prediction sub-models corresponding to the one or more feature data [paragraphs 0060-0062, “a machine learning prediction model is established according to the load characteristic … a time series characteristic of load attributes of the target cell is extracted, and different prediction algorithms are adopted for the modeling … Table 2 is a mapping table of load characteristics and prediction models”];
Bharti teaches
randomly inputting one or more feature data into a linear regression model [paragraphs 0078-0079, “linear regression is used to predict future consumption of the device … Using a linear regression model, the following mathematical equations can be used to create & predict the monthly energy or monthly time consumed for future”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for energy saving of Zhang to include a process of inputting one or more feature data into a linear regression model of Bharti. Doing so would help predicting future consumption of the device (Bharti, 0078).
Zhang and Bharti do not teach
obtaining a second prediction value corresponding to the one or more feature data according to the one or more feature data and corresponding regression prediction sub-models;
calculating a score value of each of the regression prediction sub-models according to the second prediction value and the energy efficiency indicator;
determining at least one regression prediction sub-model whose score value is greater than a preset threshold as the regression prediction model.
Jiang teaches
obtaining a second prediction value corresponding to the one or more feature data according to the one or more feature data and corresponding regression prediction sub-models [Col. 2, lines 14-15, “generate a plurality of multivariate regression models based on the base load factor”; Col. 4, line 20 – Col. 5, line 10, “The VBDD model 102 describes energy consumed in a building in terms of the energy consumed for its base load … A VBDD model is built for each energy type. The VBDD model 102 in one embodiment is a regression model built using data 108 collected about the building. Such data may include energy consumption data … In constructing the VBDD model 102, the methodology of the present disclosure in one embodiment may try different variations of, or a range of temperatures, for instance, that includes the base temperature. For example, a range between 60 to 70 degrees Fahrenheit may be used as the base temperature to try different VBDD models”; Col. 3, lines 52-55, “integrate those developed models 102,104, 106 to provide energy efficiency evaluation 112, root cause analysis 114, anomaly detection 116, and forecast of future energy usage 118”; It can be seen that multiple different regression models are used to generate prediction values (including a second prediction value)];
calculating a score value of each of the regression prediction sub-models according to the second prediction value and the energy efficiency indicator [Col. 5, lines 3-9, “a model with the best fit (e.g., the largest R2 value (R-squared value)) may be selected for use. For example, if the VBDD model with base temperature being equal to 65 degrees Fahrenheit has the largest R2, we will estimate the base temperature for the building as 65 degrees Fahrenheit and utilize this VBDD model for subsequent analysis”];
determining at least one regression prediction sub-model as the regression prediction model [Col. 5, lines 3-9, “a model with the best fit (e.g., the largest R2 value (R-squared value)) may be selected for use. For example, if the VBDD model with base temperature being equal to 65 degrees Fahrenheit has the largest R2, we will estimate the base temperature for the building as 65 degrees Fahrenheit and utilize this VBDD model for subsequent analysis”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for energy saving of Zhang to include obtaining a second prediction value corresponding regression prediction sub-models, calculating a score value of each of the regression prediction sub-models, and determining at least one regression prediction sub-model as the regression prediction model of Jiang. Doing so would help predicting energy consumption of the building with accuracy (Jiang, Col. 9, lines 9-10).
Zhang, Bharti and Jiang do not teach
determining at least one regression prediction sub-model whose score value is greater than a preset threshold as the regression prediction model (emphasis added).
Matsuoka teaches
determining at least one regression prediction sub-model whose score value is greater than a preset threshold as the regression prediction model [paragraph 0098, “compute the fit value of various different models with respect to the data and, when one or more models is associated with a fit value less than a first threshold value or with a corresponding probability value greater than a second threshold value, the intelligent controller may select the best fitting model from the one or more models and generate a parameterized plot by associating the selected model”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for energy saving of Zhang to include determining at least one regression prediction sub-model whose score value is greater than a preset threshold as the regression prediction model of Matsuoka. Doing so would help providing more accurate predictions (Matsuoka, 0103).
Claim 10 is rejected by the same reason as of claim 2, since these claims recite the similar limitations.
Claim 18 is rejected by the same reason as of claim 2, since these claims recite the similar limitations.
Claims 3, 11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. in view of Bharti et al. in view of Jiang et al. in view of Matsuoka et al. and further in view of Alamri et al. (US Pub. 2024/0185149).
As per claim 3, Zhang, Bharti, Jiang and Matsuoka teach the method for predicting energy consumption according to claim 2.
Zhang, Bharti, Jiang and Matsuoka do not explicitly teach
in response that no regression prediction sub-model has a score value that is greater than the preset threshold, regaining updated training data, and retraining the linear regression model by using the updated training data, and obtaining an updated regression prediction sub-model.
Alamri teaches
in response that no regression prediction sub-model has a score value that is greater than the preset threshold, regaining updated training data, and retraining the linear regression model by using the updated training data, and obtaining an updated regression prediction sub-model [abstract, “Forecasted values of total energy consumption of the gas processing plant are determined using the machine learning algorithms and real-time energy stream data. Forecasting models are re-trained using new data if an error between the forecasted values and actual energy demand exceeds a threshold”; paragraph 0041, “forecasting models of the machine learning algorithms are re-trained using new data if an error between the forecasted values and actual energy demand exceeds a threshold … repeated until the forecasted values and values of the actual energy demand converge”; Examiner interprets new data as the updated training data, and “an error between the forecasted values (from the models) and actual energy demand exceeds a threshold” as “no regression prediction sub-model has a score value that is greater than the preset threshold”, and it can be seen that the process of retraining with new data (updating the model) is repeated until converge].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for energy saving of Zhang to include in response that no regression prediction sub-model has a score value that is greater than the preset threshold, regaining updated training data, and retraining the linear regression model by using the updated training data, and obtaining an updated regression prediction sub-model of Alamri. Doing so would help reducing error, and accurately determining energy efficiency and emissions (Alamri, abstract).
Claim 11 is rejected by the same reason as of claim 3, since these claims recite the similar limitations.
Claim 19 is rejected by the same reason as of claim 3, since these claims recite the similar limitations.
Claims 4, 12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. in view of Bharti et al. in view of Jiang et al. in view of Matsuoka et al. in view of Alamri et al. and further in view of Singh et al. (US Pub. 2024/0255906).
As per claim 4, Zhang, Bharti, Jiang, Matsuoka and Alamri teach the method for predicting energy consumption according to claim 2.
Alamri teaches
obtaining the updated training data [abstract, “Forecasted values of total energy consumption of the gas processing plant are determined using the machine learning algorithms and real-time energy stream data. Forecasting models are re-trained using new data];
Zhang, Bharti, Jiang, Matsuoka and Alamri do not teach
obtaining the updated training data by removing data that does not conform to a preset data type from the historical energy efficiency data.
Singh teaches
obtaining the updated training data by removing data that does not conform to a preset data type from the historical energy efficiency data [paragraph 0036, “outlier historical data can be detected and removed from the historical training data, based on any suitable approaches, before using the historical training data to train the machine learning model (e.g., machine learning model 3110). System 310 can detect outlier historical data of the historical training data for the machine learning model based on a rule-based, statistical …”; Since Zhang (as modified) teaches the updated training data is obtained (Alamri, abstract), while Singh teaches the training data (historical training data) is obtained by removing the outlier data based on rule, statistical …, therefore, the combination of Zhang (as modified) and Singh teaches the above claim limitation].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for energy saving of Zhang to include obtaining the updated training data by removing data that does not conform to a preset data type from the historical energy efficiency data of Singh. Doing so would help training a prediction model to predict and reduce energy consumption (Singh, 0032).
Claim 12 is rejected by the same reason as of claim 4, since these claims recite the similar limitations.
Claim 20 is rejected by the same reason as of claim 4, since these claims recite the similar limitations.
Claims 5 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. in view of Bharti et al. and further in view of Mahishi et al. (US Pub. 2024/0330400).
As per claim 5, Zhang and Bharti teach the method for predicting energy consumption according to claim 1.
Zhang further teaches
determining a plurality of energy consumption factors related to the energy efficiency indicator [Table 1 shows indicator of weather data and the coding of the weather data in the energy-consumption influencing factor data];
determining target data from the energy efficiency data according to the plurality of energy consumption factors and the feature extraction algorithm [paragraph 0042, “the energy-consumption influencing factor data includes a variety of load information of the target cell”; paragraphs 0055-0058, “the energy-consumption influencing factor data is coded, which facilitates the extraction of load characteristics and the prediction of the load trend through modeling … the load characteristic (mainly referring to a time series characteristic of a load as a function of time) is extracted … The load characteristic includes, but is not limited to, a trend characteristic as a function of time and a periodic characteristic];
Zhang and Bharti do not teach
obtaining correlation values between the target data and the plurality of energy consumption factors;
determining the plurality of influencing factors from the target data according to a preset number and a preset sorting rule of the correlation values.
Mahishi teaches
obtaining correlation values between the target data and the plurality of energy consumption factors [abstract, “ identifying a target attribute and a plurality of influencing attributes, determining a first correlation value representing a first correlation between the target attribute and a first influencing attribute of the plurality of influencing attributes, determining a second correlation value representing a second correlation between the target attribute and a second influencing attribute of the plurality of attributes”];
determining the plurality of influencing factors from the target data according to a preset number and a preset sorting rule of the correlation values [abstract, “based on the first correlation value and the second correlation value, ranking the first influencing attribute higher than the second influencing attribute in a ranked list of the plurality of influencing attributes representing an influence of each of the plurality of influencing attributes on the target attribute”; paragraph 0023, “ased on the correlation value determined for representing an influence of each influencing attribute on the target attribute, the system selects one or more of the influencing attributes for configuration. The system may select, for example, influencing attributes with a threshold level of influence on the target attribute, or the m highest-influencing attributes of the set of influencing attributes. Configuring the influencing attributes may include, for example, determining and maintaining preferred values for the influencing attributes that correspond to the preferred values for the target attribute”; paragraph 0042, “The range selector 124 may determine the preferred range of values for all or a portion of the influencing attributes in the ranked list. For example, a preferred range of values may be determined for influencing attributes that meet a threshold level of influence on the target attribute. The range selector 124 may bypass influencing attributes that are relatively lower on the ranked list, such as influencing attributes that do not meet the threshold level of influence on the target attribute. The threshold level of influence may be selected at least in part to facilitate a determination, for example, by the range selector 124, as to whether respective influencing factors are sufficiently influential to configure a preferred range of values”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for energy saving of Zhang to include obtaining correlation values between the target data and the influencing data, determining the plurality of influencing factors of Mahishi. Doing so would help predicting a load trend of a target device based on influencing factor data (Zhang, 0006).
Claim 13 is rejected by the same reason as of claim 5, since these claims recite the similar limitations.
Claims 6 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. in view of Bharti et al. and further in view of Tyagi et al. (US Pub. 2013/0282193).
As per claim 6, Zhang and Bharti teach the method for predicting energy consumption according to claim 1.
Zhang further teaches
in responses that no user's prediction request is received, determining the regression prediction sub-model comprising the plurality of influencing factors as the regression prediction model according to the plurality of influencing factor and the energy efficiency indicator [Fig. 2, paragraphs 0051-0060, “energy-consumption influencing factor data of a target cell is collected … The energy-consumption influencing factor data includes data in a past period of time (for example, six months), and a collection time granularity may be one hour … the energy-consumption influencing factor data is coded, which facilitates the extraction of load characteristics and the prediction of the load trend through modeling … the load characteristic (mainly referring to a time series characteristic of a load as a function of time) is extracted … The load characteristic includes, but is not limited to, a trend characteristic as a function of time and a periodic characteristic. For example, in a specific time period, the load continues to rise or fall smoothly, rises or falls rapidly, fluctuates within a certain range, rises or falls regularly according to a certain period, or the like … and a model is made according to the load characteristic … a machine learning prediction model is established according to the load characteristic”; paragraph 0072, “the machine learning prediction model is established through a regression modeling algorithm”; Tables 1-2 show indicator of weather data and the coding of the weather data in the energy-consumption influencing factor data, and the mapping of load characteristics and prediction models; paragraphs 0060-0062, “a machine learning prediction model is established according to the load characteristic … a time series characteristic of load attributes of the target cell is extracted, and different prediction algorithms are adopted for the modeling … Table 2 is a mapping table of load characteristics and prediction models”].
Zhang and Bharti do not teach
in responses that a user's prediction request is received, determining at least one influencing factor from the plurality of influencing factors according to the prediction request; and determining a regression prediction sub-model comprising the at least one influencing factor as the regression prediction model according to the at least one influencing factor and the energy efficiency indicator;
Tyagi teaches
in responses that a user's prediction request is received, determining at least one influencing factor from the plurality of influencing factors according to the prediction request [paragraph 0014, “a demand response event for a participating customer. For example, a contract may specify that the utility can invoke just a limited number of events per year”; paragraph 0017, “the benefit from a DR event may be determined by the peak load reduction resulting from a response profile of the participants, which in turn may be influenced by factors, such as daily temperature, humidity, etc.”; Examiner interprets demand response event for a participating customer as the participant/consumer requests for an energy prediction during the peak period]; and determining a regression prediction sub-model comprising the at least one influencing factor as the regression prediction model according to the at least one influencing factor and the energy efficiency indicator [paragraph 0023, “the database 134 stores historical information on whether a customer site participated in a demand response event, the start time and end time, day of week, season, etc. In addition, the amount of load reduction and rebound may be stored in database 134. Data related to response forecasting and expected future benefit calculations may also be stored in database”; Fig. 3 discloses a module performs the load prediction based on an aggregate demand response profile of the demand response participants; paragraph 0017, “the benefit from a DR event may be determined by the peak load reduction resulting from a response profile of the participants, which in turn may be influenced by factors, such as daily temperature, humidity, etc.”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for energy saving of Zhang to include in responses that a user's prediction request is received, determining a regression prediction sub-model comprising the at least one influencing factor as the regression prediction model according to the at least one influencing factor and the energy efficiency indicator of Tyagi. Doing so would help adjusting an original load forecast during a peak period (Tyagi, 0032).
Claim 14 is rejected by the same reason as of claim 6, since these claims recite the similar limitations.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. in view of Bharti et al. and further in view of Feldman et al. (US Pub. 2023/0286409).
As per claim 7, Zhang and Bharti teach the method for predicting energy consumption according to claim 1.
Zhang further teaches
obtaining production data of the target device during a production process [paragraph 0006, “collecting energy-consumption influencing factor data of a target cell”];
Zhang and Bharti do not teach
performing a first-order difference calculation on the production data, and obtaining the energy efficiency data.
Feldman teaches
performing a first-order difference calculation on the production data, and obtaining the energy efficiency data [paragraph 0150, “the system determines a plurality of power transfer efficiencies of the EVSEs. Each power transfer efficiency is associated with each EVSE of the plurality of the EVSEs … To determine the power transfer efficiency of each EVSE, the controller, and/or the system computing device 116 may store, utilize, and/or selectively execute the first-order efficiency approximation algorithm”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for energy saving of Zhang to include performing a first-order difference calculation on the production data, obtaining the energy efficiency data of Feldman. Doing so would help determining a plurality of power transfer efficiencies (Feldman, 0150).
Claim 15 is rejected by the same reason as of claim 7, since these claims recite the similar limitations.
Claims 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang et al. in view of Bharti et al. and further in view of Jovanovic et al. (US Pub. 2018/0290123).
As per claim 8, Zhang and Bharti teach the method for predicting energy consumption according to claim 1.
Bharti teaches
the trend graph of energy consumption comprises a curve formed by the first predicted value at each moment in the preset time period [paragraph 0047, “FIG. 4 depicts an example graph illustrating trends, by-month, in energy consumed by an electrical device across multiple (5) years. Lines indicated by reference numerals 402-424 are paired with the twelve months of the year as follows: 402—January; 404—February; 406—March; 408—April; 410—May; 412—June; 414—July; 416—August; 418—September; 420—October; 422—November; 424—December”];
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for energy saving of Zhang to include the trend graph of energy consumption comprises a curve formed by the first predicted value at each moment in the preset time period of Bharti. Doing so would help monitoring energy efficiency performance of electrical devices (Bharti, paragraph 0015).
Zhang and Bharti do not teach
the trend graph of energy consumption comprises a curve formed by the energy efficiency indicator.
Jovanovic teaches
the trend graph of energy consumption comprises a curve formed by the energy efficiency indicator [paragraph 0048, “a bar graph illustrating energy efficiency for CO.sub.2 reduction using a corona discharge reactor according to the present invention at various different feed (20, 50 and 50 standard cubic centimeters per minute rates”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have modified the method for energy saving of Zhang to include the trend graph of energy consumption comprises a curve formed by the energy efficiency indicator of Jovanovic. Doing so would help monitoring energy efficiency performance of electrical devices.
Claim 16 is rejected by the same reason as of claim 8, since these claims recite the similar limitations.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Johnson et al. (US Patent 10,443,873) describes a method for monitoring an HVAC system.
Magori (US Patent 10,083,408) describes a method for saving energy at the time of operating a building.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TRI T NGUYEN whose telephone number is 571-272-0103. The examiner can normally be reached M-F, 8 AM-5 PM, (CT).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, OMAR FERNANDEZ can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TRI T NGUYEN/Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
