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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 4, 5, and 7-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With regards to Claim 1, the limitation “monitoring the equipment's power consumption in the building according to the output results of the physics-informed neural network model” is indefinite as it is unclear how the monitoring would depend on the output results. The Specification is silent on such dependency.
For the purpose of a compact prosecution, the Examiner did not give patentable wait to the feature “according to the output results of the physics-informed neural network model”.
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, 4, 5, and 8-9 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.
Specifically, representative Claim 1 recites:
“A non-intrusive load monitoring method based on physics-informed neural network, wherein,
comprising the following steps:
Step 1, obtaining a total load data and an equipment load data of a building in a certain period of time, and using a sliding window method to cut to construct a training data;
Step 2, designing a deep learning neural network model to learn the equipment load characteristics contained in the total load data, and outputting the equipment load forecasting;
Step 3, based on a physics-constrained learning framework, training the deep learning neural network model by iteratively optimizing the training loss to obtain a trained physics-informed neural network model; and Step 4, monitoring the equipment's power consumption in the building according to the output results of the physics-informed neural network model;
wherein, the step 1 is as follows:
Step 1.1, collecting an active power P.sub.t.sub.0.sub.:t.sub.n and a reactive power Q.sub.t.sub.0.sub.:t.sub.n of the total load in the certain period of time, and an active power P.sub.t.sub.0.sub.:t.sub.n.sup.i and a reactive power Q.sub.t.sub.0.sub.:t.sub.n.sup.i of the equipment load, and then obtaining a total load sample M=[P.sub.t.sub.0.sub.:t.sub.n,Q.sub.t.sub.0.sub.:t.sub.n] and an equipment load sample L.sup.i=[P.sub.t.sub.0.sub.:t.sub.n.sup.i,Q.sub.t.sub.0.sub.:t.sub.n.sup.i], where i is the equipment number;
Step 1.2, using the sliding window with width w and =step size l to cut M and L.sup.i, constructing the training data U.sup.i={M.sub.train,L.sub.train.sup.i} of the equipment i, where,
M.sub.train={[P.sub.t.sub.j.sub.:t.sub.j+w,Q.sub.t.sub.j.sub.:t.sub.j+w]|j=0, . . . ,n−w}
L.sub.train.sup.i={[P.sub.t.sub.j.sub.:t.sub.j+w.sup.i,Q.sub.t.sub.j.sub.:t.sub.j+w.sup.i]|j=0, . . . ,n−w}; wherein, the step 2 is as follows: Step 2.1, inputting the training data U.sup.i into the following deep learning neural network respectively:
h.sub.0=U.sup.i
h.sub.m=Φ(W.sub.m.Math.h.sub.m-1+b.sub.m) where, h.sub.0 is the original input of the input layer of the constructed deep learning neural network, h.sub.m, W.sub.m and b.sub.m are respectively the output, weight and bias of the m th hidden layer of the neural network model, and Φ(⋅) is the activation function;
Step 2.2, designing the following output layer for learning:
F.sup.i=Ψ(W.sub.M.Math.h.sub.M+b.sub.M) where, F.sup.i=[{circumflex over (P)}.sub.t.sub.j.sub.:t.sub.j+w.sup.i,{circumflex over (Q)}.sub.t.sub.j.sub.:t.sub.j+w] is the load forecasting of equipment i, h.sub.M is the output of the last hidden layer of the network, W.sub.M and b.sub.M is the weight and bias of the output layer respectively, and Ψ(⋅) is the activation function; wherein, the step 3 is as follows: firstly, according to the physical relationship between powers, calculating a physical constraints violation loss loss.sub.p.sup.i of the deep learning neural network model corresponding to the equipment i that is
losspi=.Math.j=0n-w.Math.(P^tj:tj+wi)2+(Q^tj:tj+wi)2-(Ptj:tj+wi)2+(Qtj:tj+wi)2.Math.
then, calculating a prediction deviation loss loss.sub.f.sup.i of the deep learning neural network model corresponding to the equipment i that is
loss.sub.f.sup.i=E(F.sup.i,L.sub.train.sup.i); where, E is the difference measurement function;
finally, calculating the training loss of the constrained physics-informed neural network model by weighted summation:
loss.sub.total.sup.i=loss.sub.f.sup.i+ω.sub.p.Math.loss.sub.p.sup.i where, ω.sub.p is the weight coefficient of the physical constraints violation loss;
wherein, the step 4 is as follows:
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The claim limitations in the abstract idea have been highlighted in bold above, including all newly amended (8/6/2025) step 4 features; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
The above claim comprise the following additional elements:
A non-intrusive load monitoring method based on physics-informed neural network, Step 1.1, collecting an active power and a reactive power of the total load in the certain period of time, and an active power and a reactive power of the equipment load, and then obtaining a total load sample and an equipment load sample, Step 4, monitoring the equipment's power consumption in the building.
The additional elements in the preamble are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
The additional elements such as collecting power data (active, reactive) of the total load and equipment are generally recited/not meaningful and represent insignificant extra-solution activity of mere data gathering to the judicial exception. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does not integrate the judicial exception into a practical application”. Similarly, the step of monitoring the equipment's power consumption in the building represents insignificant post-solution activity and not meaningful to indicate a practical application.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record. For example,
Gaëlle Rebec et al. (US 9558404) and JONG-WOONG CHOE et al. (CN 105143892) discloses collecting active and reactive power data and monitoring power/energy consumption/data;
Selim MIMAROGLU et al. (US 20210158186), Choe, and Norford et al., “Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms”, Energy and Buildings, 1996, pp. 51-64, vol. 24, disclose non-intrusive load monitoring;
Charles Howard Cella et al. (CN 113272850) and Hao Zhiu et al. (CN 113363979) discloses training the deep learning neural network model.
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 4, 5, and 7-9 provide additional features/steps which are part of an expanded abstract idea of the independent claims and, therefore, these claims are not eligible without meaningful additional elements that reflect a practical application and/or qualified for significantly more for substantially similar reasons as discussed with regards to Claim 1.
Examiner Note with Regards to Prior Art of Record
Claims 1, 4, 5, and 8-9 are distinguished over prior art of record based on the reasons below.
The following references are considered to be the closest prior art to the claimed invention:
Norford et al., “Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms”, Energy and Buildings, 1996, pp. 51-64, vol. 24, discloses non-intrusive load monitoring of individual loads in a building based on changes in steady-state power patterns as compared to known patterns.
Veronica Piccialli et al., “Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network”, Energies 2021, 14, 847, 16 pages, discloses Non-Intrusive Load Monitoring (NILM) as the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances. The authors propose a deep neural network that combines a regression subnetwork with a classification subnetwork for solving the NILM problem.
Hua QIAN et al. (CN 111045861) discloses a sensor-based data recovery method of the deep neural network including obtaining the loss function of each set of training data. In one possible implementation, in the iterative training, a loss function is constituted based on a weighted sum of loss functions.
Gaëlle Rebec et al. (US 9558404) and JONG-WOONG CHOE et al. (CN 105143892) discloses collecting active and reactive power data and monitoring power/energy consumption/data.
Charles Howard Cella et al. (CN 113272850) and Hao Zhiu et al. (CN 113363979) discloses training the deep learning neural network model.
Selim MIMAROGLU et al. (US 20210158186) discloses non-intrusive load monitoring; a trained machine learning model configured to disaggregate device energy usage from household energy usage can be stored, where the machine learning model is trained to predict energy usage for a target device from household energy usage.
Shuhui Li et al. (US 20150248118) discloses modeling HVAC energy consumption of a house using a learning based approach that is based on actual energy usage data collected over a period of days.
However, in regards to Claim 1, the claims differ from the above closest prior art, either singularly or in combination, because the references fail to anticipate or render obvious the claimed multi-step algorithm including Steps 1.1, 1.2, 2.1, and 2.2, in combination with all other limitations in the claim as claimed and defined by applicant.
Response to Arguments
Applicant's arguments filed 8/6/2025 have been fully considered but they are not persuasive.
35 USC § 112
The Applicant argues (p.6): It is respectfully submitted that in view of the present amendments, the rejection has been overcome. In particular, Claim 1 has been amended to address the information points out by the Examiner and other informalities. More specifically, Claim 1 has been amended to particularly define the step pf monitoring the equipment’s power consumption in the building according to the output results of the physics-informed neural network model by specifying the steps of: “giving any time of the building as a starting point …
The Examiner submits that the argument does not address the examiner’s concerns (NFOA, p.2). It is still unclear, even with amended features, how is the monitoring process performed based on the output results when the amended features do not discuss monitoring steps. “The output results of the power consumption of each equipment load” are developed by modeling of a total load sample using a neural network.
35 USC § 101
The Applicant argues (p.7-8): Applicant respectfully submits that the Examiner failed to look at the additional limitations as an ordered combination. In fact, the Examiner improperly focused on each additional limitation individually. These instruments and the information they generated during the steps provided essential and great benefits to improve load monitoring accuracy in power grids yet remain non-intrusive with additional benefits of embedding physical constrains, which is the great advantages the present invention over prior art.
In addition, the additional limitations are absolutely NOT “merely add[ing] insignificant extra-solution activity to” the invention. The combination of limitations ARE the solution of the present invention, rather than EXTRA-solution activity. The additional limitations as defined by the pending claims, include using non-intrusive load monitoring device, one or more processors, non-transitory computer readable medium, etc. The invention as a whole amount to significantly more than the judicial exception.
The Examiner respectfully submits that there is no combination of additional elements qualified for significantly more. The non-intrusive load monitoring device, one or more processors (dependent Claim 8), non-transitory computer readable medium (dependent Claim 9) are all recited in generality and do not amount for a particular machine or qualified for significantly more. As discussed in MPEP 2106. 04(d): “The courts have also identified limitations that did not integrate a judicial exception into a practical application: “ …merely using a computer as a tool to perform an abstract idea, as discussed in MPEP § 2106.05(f)”.
The argued improvements of “essential and great benefits to improve load monitoring accuracy in power grids” is accomplished using abstract idea steps which does not demonstrate a practical application.
The Applicant argues (p.9): The courts have also found that improvements in technology beyond computer functionality may demonstrate patent eligibility. In McRO, the Federal Circuit held claimed methods of automatic lip synchronization and facial expression animation using computer- implemented rules to be patent eligible under 35 U.S.C. 102, because they were not directed to an abstract idea. … The basis for the McRO court's decision was that the claims were directed to an improvement in computer animation and thus did not recite a concept similar to previously identified abstract ideas. Jd.
… The amended claims of the present invention recite a combination of additional elements. The claims as a whole are practical applications including specific improvement over prior art. Thus, the claims are eligible for patent protection and are not directed to abstract ideas.
Please refer to the following analysis of Example 40 of The Subject Matter Eligibility Examples (Adaptive Monitoring of Network Traffic Data), wherein under step 2A analysis, the claim is integrated into a Practical Application because “[t]he claim recites the combination of additional elements of collecting at least one of network delay, packet loss, or jitter relating to the network traffic passing through the network appliance, and collecting additional Netflow protocol data relating to the network traffic when the collected network delay, packet loss, or jitter is greater than the predefined threshold.”
The Examiner submits that no improvement in computer technology similar to McRO is recited in the claims. MPEP 2106.05(a).II: “The McRO court also noted that the claims at issue described a specific way (use of particular rules to set morph weights and transitions through phonemes) to solve the problem of producing accurate and realistic lip synchronization and facial expressions in animated characters, rather than merely claiming the idea of a solution or outcome, and thus were not directed to an abstract idea.”
The Examiner also submits that no combination of additional elements are recited either unlike in Example 40 and as also acknowledged by the Applicant (“he claim recites the combination of additional elements of collecting at least one of network delay, packet loss, or jitter relating to the network traffic passing through the network appliance, and collecting additional Netflow protocol data”). The Examiner also notes that the Applicant did not present specific argument which additional elements would be included in such “combination”.
The Applicant argues (p.10) the alleged similarity to eligible claims in BASCOM, Thales, and DDR Holdings.
The Examiner respectfully disagrees that the similarity exists in either case.
In BASCOM, the court found that an inventive concept can be found in the ordered combination of claim limitations (installation of a filtering tool at a specific location, remote from the end-users, with customizable filtering features specific to each end user) that transformed the abstract idea of filtering content into particular, practical application of that abstract idea. However, the current claims do not recite any inventive combination of elements that would qualify for “significantly more” like in BASCOM.
In Thales, the claims were not directed to an abstract idea. There, the eligibility conclusion was reached due to unconventional configuration of sensors. The claims specify a particular configuration of inertial sensors and a particular method of using the raw data from the sensors in order to more accurately calculate the position and orientation of an object on a moving platform. The mathematical equations are a consequence of the arrangement of the sensors and the unconventional choice of reference frame in order to calculate position and orientation. Far from claiming the equations themselves, the claims seek to protect only the application of physics to the unconventional configuration of sensors as disclosed.”
The Examiner submits that unlike in the DDR case, the current invention is not “rooted in the computer technology” where the claim “…recites a specific way to automate the creation of a … web page”.
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 extension fee 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER SATANOVSKY whose telephone number is (571)270-5819. The examiner can normally be reached on M-F: 9 am-5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached on (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ALEXANDER SATANOVSKY/
Primary Examiner, Art Unit 2863
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