DETAILED ACTION
Introduction
This Office action is responsive to the communications filed February 18, 2026 Claims 1-20 are pending.
Response to Arguments
Applicant's arguments filed February 18, 2026 have been fully considered but they are not persuasive.
1. Applicant submits that the claims are not directed to an abstract idea. It is asserted that the claims do not “recite features that fall within the subject matter grouping of at least one of : mathematical concept, mental processes, and certain methods of organizing human activity.” Applicant argues the claims improve the technology by “modifying a machine learning model by injecting features after the machine learning model generates latent space variable results in the machine learning model generating more accurate predictions that are based on physics relationships between features and the power generation device.” See p. 9-14 of Remarks.
However, the Examiner respectfully disagrees. As indicated in the rejection, the claims are directed to an abstract idea of collecting and processing known information. Hence, the underlying functions of the claim are directed to a mental process. As per the recitation of training the machine learning model is “generally linking the use of a judicial exception to a particular” field of use. MPEP 2106.05(h). The “additional element or combination of elements must do ‘more than simply stat[e]the [judicial exception] while adding the words ‘apply it’.” MPEP 2106.05(f). Also, inputting abstract concepts to generate “more accurate predictions” is not considered a technologically improved machine learning model.
2. Applicant asserts that Kim fails to disclose “the predictor receiving a combination of (i) information from the latent space and (ii) a second set of inputs to generate the power demand prediction.”
However, the Examiner respectfully disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Claim 1 recites “processing a first potion of the plurality of features by a first portion of a machine learning model to generate one ore more latent space variables; processing the one or more latent space variables and a second portion of the plurality of features by a second portion of the machine learning model to generate a predicated power output of the at least one power generation device.” The Office action cites Kim at Fig. 4 and abstract. Also, it was noted that “Maheswari expressly disclose the power generation device and processing the one or more latent space variables and a second portion of the plurality of features by a second portion of the machine learning model to generate a predicated power output of the at least one power generation device…” (see Non-Final at p.7).
Figure 4 of Kim illustrates power encode Ep and auxiliary encoder Ea receiving information p and a. The inputs are embedded into the latent space, which are then received by the predictor (P). Hence, “ (i) information from the latent space and (ii) a second set of inputs to generate the power demand prediction” are received. Additionally, Maheswari discloses these features at paragraphs [0033], [0034], and [0038].
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In the instant case, claims 1-13 is directed to a method. Claims 14-19 are directed to a non-transitory computer-readable. Claim 20 is a system. Therefore, these claims fall within the four statutory categories of invention.
For example, claim 1 recites an abstract idea of collecting and processing known information. The claim under its broadest reasonable interpretation recites limitations grouped within the “mental processes” grouping of abstract ideas. The "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper"). See MPEP § 2106.04(a)(2), subsection III.
The claim limitations reciting the abstract idea are grouped within the “method processes” grouping of abstract ideas as they relate to collecting and processing known information. More specifically, the following the bolded claim elements recite additional elements while the other claim elements recite the abstract idea. according to MPEP 2106.04(a).
A computer-implemented method, comprising:
receiving one or more data samples associated with at least one power generation device, each data sample including a plurality of features and a measured power output of the at least one power generation device;
processing a first portion of the plurality of features by a first portion of a machine learning model to generate one or more latent space variables;
processing the one or more latent space variables and a second portion of the plurality of features by a second portion of the machine learning model to generate a predicted power output of the at least one power generation device; and
training the machine learning model based on the measured power output and the predicted power output.
Independent claims 5,14, and 20 recite similar language.
This judicial exception is not integrated into a practical application because, when analyzed under prong two of step 2A of the Alice/Mayo test (See MPEP 2106.04(d)), the additional element(s) of the claim(s) such as the power generation device and machine learning model are merely used as tools to perform an abstract idea and/or generally link the use of a judicial exception to a particular technological environment. Specifically, these additional elements perform the steps or functions of collecting and processing known information. Viewed as a whole, the use of power generation device and machine learning model as tools to implement the abstract idea and/or generally linking the use of the abstract idea to a particular technological environment does not integrate the abstract idea into a practical application because it requires no more than a computer or computer networks performing functions that correspond to acts required to carry out the abstract idea. The additional elements do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea, and the claims are directed to an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when analyzed under step 2B of the Alice/Mayo test (See MPEP 2106.05), the additional element(s) of the power generation device and machine learning model to perform the steps amounts to no more than using generic hardware or software to automate and/or implement the abstract idea of collecting and processing known information. Viewed as a whole, the combination of elements recited in the claims merely recite the concept of collecting and processing known information. Therefore, the use of these additional elements does no more than employ the computer as a tool to automate and/or implement the abstract idea. The use of a computer or processor to merely automate and/or implement the abstract idea cannot provide significantly more than the abstract idea itself (MPEP 2106.05 (f) & (h)). Therefore, the claim is not patent eligible.
The dependent claims further describe the abstract idea such as:
retraining the machine learning model based on one or more updated data samples;
retraining the machine learning model based on an updated configuration of one or both of the first portion of the machine learning model or the second portion of the machine learning model;
inputting, to the second portion of the machine learning model, a concatenation of the one or more latent space variables and the second portion of the plurality of features and (b) the scaled one or both of the one or more latent space variables or the second portion of the plurality of features;
masking an output of the second portion of the machine learning model based on a mask parameter, wherein the mask parameter is based on a time of day associated with the plurality of features;
clipping an output of the second portion of the machine learning model based on a clipping parameter, wherein the clipping parameter is based on a power output maximum;
transmitting a message including the predicated power output of the at lest one power generation device; and
generating an alert based on the predicated power output of the at least one power generation device and one or more alert thresholds.
The dependent claims do not include additional elements that integrate the abstract idea into a practical application or that provide significantly more than the abstract idea. Therefore, the dependent claims are also not patent eligible.
Claim Rejections - 35 USC § 103
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 1-3, 5-7, 14-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over “Explainable prediction of electric energy demand using a deep autoencoder with interpretable latent space” to Kim et al. (“Kim”) in view of U.S. Publication No. 2023/0214703 to Maheswari et al. (“Maheswari”).
As per claim 1, Kim discloses receiving one or more data samples associated with at least one power generation device, each data sample including a plurality of features and a measured power output of the at least one power generation device (p.2 input x; Fig. 4 at p. 4 – receive primary power demand information p);
processing a first portion of the plurality of features by a first portion of a machine learning model to generate one or more latent space variables (Fig. 4 at p. 4 – input p to power encoder Ep to power latent space Zp);
processing the one or more latent space variables and a second portion of the plurality of features by a second portion of the machine learning model to generate a predicted power output (abstract – deep autoencoder processes latent variables; power encoder processes the power information/plurality of features; explainer that provides the most important input features in predicting the future demand by utilizing the interpretable latent variables); and
training the machine learning model based on the measured power output and the predicted power output (page 2 – main power demand information is embedded in a high dimensional space, but auxiliary information, excluding the main power demand information, is separately embedded in a two-dimensional space…the proposed model is trained).
As for processing the variables and features to generate a predicted power output of the at least power generation device, Kim discloses the processing, but does not expressly recite a power generation device. However, the reference describes previously studies that receive electrical meter data from non-residential buildings, which is a power generation device (p. 3).
Maheswari expressly disclose the power generation device and processing the one or more latent space variables and a second portion of the plurality of features by a second portion of the machine learning model to generate a predicted power output of the at least one power generation device (abstract; paragraphs [0005]- the monitored system may be embodied as a collection of mechanical components, electrical components, and other components that collectively operate as an energy generator. The monitored system may be embodied as, for example, one or more wind turbines, one or more solar panels, or some other energy generating asset. [0034], [0038]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Kim to include the power generation device of Maheswari. Applying the known technique of Maheswari into the system of Kim would have been recognized by those of ordinary skill in the art as resulting in an improved system that would have yielded predictable results.
As per claim 2, Maheswari discloses wherein the one or more latent space variables and the second portion of the plurality of features are based on one or more physics relationships of the at least one power generation device (paragraph [0049] – solar irradiation).
As per claim 3, Kim in combination with Maheswari disclose retraining the machine learning model based on one or more updated data samples (see at least Maheswari at paragraph [0050] – using the current meteorological data as an input to the trained model; paragraph [0019] - a model may be continuously, periodically, or occasionally updated).
As per claim 5, Kim discloses receiving one or more data samples associated with at least one power generation device, each data sample including a plurality of features (p.2 input x; Fig. 4 at p. 4 – receive primary power demand information p);
processing a first portion of the plurality of features by a first portion of a machine learning model to generate one or more latent space variables (Fig. 4 at p. 4 – input p to power encoder Ep to power latent space Zp);
processing the one or more latent space variables and a second portion of the plurality of features by a second portion of the machine learning model to generate a predicted power output (abstract – deep autoencoder processes latent variables; power encoder processes the power information/plurality of features; explainer that provides the most important input features in predicting the future demand by utilizing the interpretable latent variables); and
As for processing the variables and features to generate a predicted power output of the at least power generation device, Kim discloses the processing, but does not expressly recite a power generation device. However, the reference describes previously studies that receive electrical meter data from non-residential buildings, which is a power generation device (p. 3).
Maheswari expressly disclose the power generation device and processing the one or more latent space variables and a second portion of the plurality of features by a second portion of the machine learning model to generate a predicted power output of the at least one power generation device (abstract; paragraphs [0005]- the monitored system may be embodied as a collection of mechanical components, electrical components, and other components that collectively operate as an energy generator. The monitored system may be embodied as, for example, one or more wind turbines, one or more solar panels, or some other energy generating asset. [0034], [0038]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Kim to include the power generation device of Maheswari. Applying the known technique of Maheswari into the system of Kim would have been recognized by those of ordinary skill in the art as resulting in an improved system that would have yielded predictable results.
Claim 6 is rejected on the same rationale as claim 2 above.
As per claim 7, Kim discloses inputting, to the second portion of the machine learning model, a concatenation of the one or more latent space variables and the second portion of the plurality of features (p. 4 - The predictor P concatenates the latent variables Zp and Za, which are from each latent space, uses them as input values, and finally predicts the future power demand.)
Claim 14 is rejected on the same rationale as claim 5.
Claim 15 is rejected on the same rationale as claim 2.
Claim 16 is rejected on the same rationale as claim 7.
Claim 20 is rejected on the same rationale as claim 5.
Claims 4, 10, 11, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kim and Maheswari as applied to claims 1 and 5 above, and further in view of TW-202209095-A to Khedekar et al. (“Khedekar”)
As per claim 4, Kim in view of Maheswari disclose retaining the machine learning model (see claim 3 above). The references do not expressly disclose retraining the machine learning model based on an updated configuration of one or both of the first portion of the machine learning model or the second portion of the machine learning model.
Khedekar discloses retraining a machine learning model based on an updated configuration of one or both of the first portion of the machine learning model or the second portion of the machine learning model (p. 5 of translation - The process data retains the machine learning model to be updated over time, and wherein the retraining includes configuring the machine learning model to fine-tune using one or more drift calibrations).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include the elements of Khedekar into the system of Kim in combination with Maheswari. Hence, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 10, Kim in view of Maheswari disclose the method of claim 5. The references do not expressly disclose masking an output of the second portion of the machine learning model based on a mask parameter, wherein the mask parameter is based on a time of day associated with the plurality of features.
Khedekar discloses masking an output of the second portion of the machine learning model based on a mask parameter, wherein the mask parameter is based on a time of day associated with the plurality of features (p. 19 of translation - This effect can be represented by step acceleration (ie, magnitude) and step time (ie, phase), both of which can be included in the control input parameters used to train the machine learning model… One or more of the infiltration mask control input parameters can also be used to train the machine learning model.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include the elements of Khedekar into the system of Kim in combination with Maheswari. Hence, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 11, Kim in view of Maheswari disclose the method of claim 5. The references do not expressly disclose clipping an output of the second portion of the machine learning model based on a clipping parameter, wherein the clipping parameter is based on a power output maximum.
Khedekar discloses clipping an output of the second portion of the machine learning model based on a clipping parameter, wherein the clipping parameter is based on a power output maximum (p. 20 of translation – example 2 – wafer heating correction).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to include the elements of Khedekar into the system of Kim in combination with Maheswari. Hence, since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 19 is rejected on the same rationale as claim 10.
Claims 8, 9, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Kim and Maheswari as applied to claims 5 and 14 above, and further in view of U.S. Publication No. 2021/0049460 to Ahn et al. (“Ahn”).
As per claim 8, Kim in combination with Maheswari disclose the method of claim 5. The references do not expressly disclose scaling, by a scaling parameter, one or both of the one or more latent space variables or the second portion of the plurality of features.
Ahn discloses scaling, by a scaling parameter, one or both of the one or more latent space variables or the second portion of the plurality of features (paragraphs [0339] and [0389] – Mix Max Scaler).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Kim in combination with Maheswari with the scaler of Ahn. Applying the known technique of Kim in combination with Maheswari with the scaler of Ahn would have been recognized by those of ordinary skill in the art as resulting in an improved system that would have yielded predictable results.
As per claim 9, Kim discloses inputting, to the second portion of the machine learning model, a concatenation of (a) one or both of the one or more latent space variables or the second portion of the plurality of features and (b) the scaled one or both of the one or more latent space variables or the second portion of the plurality of features (p. 4 - The predictor P concatenates the latent variables Zp and Za, which are from each latent space, uses them as input values, and finally predicts the future power demand.)
Claim 17 is rejected on the same rationale as claim 8.
Claim 18 is rejected on the same rationale as claim 9.
Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kim in combination with Maheswari as applied to claim 5 above, and further in view of CN113424221A to Hashimoto et al. (“Hashimoto”).
As per claim 12, Kim in combination with Maheswari disclose the method of claim 5. The references do not expressly disclose transmitting a message including the predicted power output of the at least one power generation device.
Hashimoto discloses transmitting a message including the predicted power output of the at least one power generation device (p. 33 of translation – the result of the prediction to output a warming and other specific message …the output information related to the predicted result).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Kim in combination with Maheswari message of Hashimoto. Applying the known technique of Kim in combination with Maheswari with message of Hashimoto would have been recognized by those of ordinary skill in the art as resulting in an improved system that would have yielded predictable results.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/Jalatee Worjloh/Primary Examiner, Art Unit 3697
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