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 . 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.
DETAILED ACTION
The following NON-FINAL Office action is in response to Applicant’s request for continued examination filed on 04/17/2026.
Status of Claims
Claims 1-2, 8-12, 16-17 and 19-20 have been amended. Claims 7 and 15 have been canceled.
Claims 1-6, 8-14 and 16-20 are currently pending and have been rejected as follows.
Continued Examination under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/17/2026 has been entered.
Response to Arguments / Arguments
Applicant’s 04/17/2026 amendment necessitated the new grounds of rejection in this office action.
Response to Applicant’s rebuttal arguments on 35 USC 101
Examiner reincorporates all findings and rationales at Final Act 10/16/2025 p.2-p11, p14-p21 ¶2.
Step 2A prong one - Mental Processes: Remarks 04/17/2026 p.9 ¶4-p.10 ¶1 argues inputting specific dataset recited as “the data associated with the consumption of the commodity, and the number of users at the location”, “into a machine learning model” “trained with set of consumption data and a number of users for a specific location, and which outputs a numeric prediction value” cannot be properly interpreted as being directed towards a mental process.
Examiner fully considered SME argument 1-mental processes but respectfully disagrees.
-> i. First, as an issue of claim construction, the Examiner tests the two wherein limitations of: “wherein the machine learning model is trained” and “wherein the machine learning model outputs a forecast” at independent Claims 1,11,17 on MPEP 2111.04 I, and finds they are not part of the steps, in active voice, of independent Claim 1, nor are they the result of execution of the one or more processors components of the non-transitory media or product of independent Claim 11, and the meter or system of independent Claim 17.
-> ii. Second, the Examiner finds that, given its breadth, the amended inputting into a machine learning model of consumption and its associated data of users or consumers at the consumption location, is not meaningfully different than human eyes reading, names of residents at the point of consumption, and periodically reading on the meter, consumption data, to be learned, absorbed, soaked-in or trained, into the biological neural network of the human brain, to deduce, infer or forecast future consumption, in a manner not meaningfully different than how
"the machine learning model is trained based on previously recorded data associated with the consumption of the commodity" and "outputs a forecast of future consumption" at the two wherein limitation just scrutinized above. These latter recitations are not meaningfully different to the capabilities of a human to vocalize, depict or output on paper the forecasted future consumption
subsequent to the aforementioned deducing, inferring or forecasting of future consumption using the biological neural network of the huma brain. It then becomes increasingly clear that here, the capabilities of the “machine learning” as broadly claimed, is not meaningfully different than the cognitive capabilities of the biological neural network of the human brain. In as such, the mere recitation of the “machine learning” at the two wherein limitations tested above [MPEP 2111.04 I] would not preclude such claims to recite or at a minimum describe or set forth the abstract mental processes or at least the computer aided mental processes of MPEP 2106.04(a)(2) III C #1,#2,#3.
Accordingly, there is a preponderance of evidence demonstrating that the features argued by Applicant Remarks 04/17/2026 p.9 ¶4-p.10 ¶1 fall within the abstract observation, evaluation, judgment and opinion enumerated by MPEP 2106.04(A)(2) III ¶2 as examples within the broad abstract grouping of mental processes.
-> iii. Third, and equally important, just because claims do recite, "machine learning", as opposed to biological learning trained on a network of biological neurons, does not immediately render the claims less abstract and eligible. For one, the Examiner points to Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025), as cited by PTAB Appeal 2025-003304,
where the Federal Circuit ruled that: “The requirements that the machine learning model be ‘iteratively trained’ or dynamically adjusted based on real time changes do not represent a technological improvement” at least because they are “incident to the very nature of machine learning”. Thus here, the “inputting” “the data associated with the consumption of the commodity, and the number of users at the location”, “into a machine learning model” stands along with the recitations of "wherein the machine learning model is trained based on previously recorded data associated with the consumption of the commodity by the plurality of consumption devices at the location", "and" "wherein the machine learning model outputs a forecast of future consumption" as features incident to the very nature of machine learning given the legal finding of Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025) supra. Such legal fining is reflected in MPEP 2106.04(a)(2) III C stating that the grouping of mental processes is broad enough to encompass instances, where the mental processes are executed in a computer environment as demonstrated by MPEP 2106.04(a)(2) III C #2 or instances of using of a computer as a tool to perform the mental processes (MPEP 2106.04(a)(2) III C #3. Here, the argued “machine learning” would represent such a computer environment or tool upon which the abstract processes of inputting or collection, and forecasting or analysis, and displaying or output of certain results of the collection and analysis taking the form of "a forecast of future consumption". Yet, MPEP 2106.04(a)(2) II A is clear that collecting information, analyzing it, and displaying certain results of the collection and analysis, represent examples of mental processes, citing Electric Power Group v. Alstom, S.A,830 F.3d 1350,1353-54,119 USPQ2d 1739,1741-42 (Fed Cir. 2016).
Based on the preponderance of legal evidence above, the Examiner submits that an argument can be made that the claims still recite, describe or set forth the abstract mental processes within which the "machine learning” to perform the abstract processes identified above or at most processes representing the very nature of machine learning, cultivating in the
outputs and transmission of "a forecast of future consumption" to market participants, namely
a "utility supplying the commodity or to a user" [consumer] "associated with the metering device"
As such, the argued "machine learning” would represent a computer environment or tool in the processes identified above, which according to MPEP 2106.04(a)(2) III C #2, #3 would not preclude the claims to recite, describe or set forth the abstract exception. At a bare minimum such, "machine learning” would represent an additional element which as later tested below, would still not render the claims less abstract and eligible.
As per Example 39, raised by Remarks 04/17/2026 p.9 ¶1, Examiner submits that it, along with all other 101 examples provided by USPTO merely hypothetical and non-precedential. See USPTO “2019 PEG, 101 Examples 37-42 document entitled “Subject Matter Eligibility Examples: Abstract Ideas” p.1 ¶1 2nd sentence. “The examples below are hypothetical and only intended to be illustrative of the claim analysis under the 2019 PEG” corroborating “May 2016 Update: Memorandum- Formulating a Subject Matter Eligibility Rejection and Evaluating the Applicant’s Response to a Subject Matter Eligibility Rejection”, p.5 ¶2 Section C: “USPTO issued examples in conjunction with the Interim Eligibility Guidance, including […] July 2015 Update Appendix I: Examples […]; These examples, many of which are hypothetical, were drafted to show exemplary analyses under the Interim Eligibility Guidance and are intended to be illustrative of the analysis only. While some of the fact patterns draw from U.S. Supreme Court and U.S. Court of Appeals for the Federal Circuit decisions, the examples do not carry the weight of court decisions. Therefore, the examples should not be used as a basis for a subject matter eligibility rejection.
Also, separate from the fact that Example 39 is hypothetical and non-precedential, the Examiner also finds that here, the "inputting" of: "the data associated with the consumption of the commodity, and the number of users at the location" and subsequent recitation of "wherein the machine learning model is trained based on previously recorded data associated with consumption of the commodity" and "wherein the machine learning model outputs a forecast of future consumption" are irreconcilably different than the technological details provided by the hypothetical and nonprecedential USPTO’s Example 39 in “applying one or more transformations to each digital facial image including mirroring, rotating, smoothing, or contrast reduction to create a modified set of digital facial images; creating a first training set comprising the collected set of digital facial images, the modified set of digital facial images, and a set of digital non-facial images; training the neural network in a first stage using the first training set”. This is because here, unlike the facial detection in Example 39, the alleged improvements address consumption and consumers data to output a forecast of future consumption of a commodity. Thus here, the asserted improvements are at best economic and abstract, not technological. The current claims have nothing to do with the facial detection by “applying one or more transformations to each digital facial image including mirroring, rotating, smoothing, or contrast reduction to create a modified set of digital facial images; creating a first training set comprising the collected set of digital facial images, the modified set of digital facial images, and a set of digital non-facial images; training the neural network in a first stage using the first training set” as was the case in the hypothetical and non-precedential Example 39.
Therefore, the Step 2A prong one - Mental Processes is found unpersuasive.
Step 2A prong one -Certain Methods of Organizing Human activities: Remarks 04/17/2026 p.10 ¶2-p.11 ¶2 points to Original Specification ¶ [0020], ¶ [0051]- ¶ [0054] ¶ [0078]-¶ [0080] to argue claim 1 recites a method that involves a metering device determining consumption amount by a plurality of consumption devices, determining an amount of people at a location and inputting the these determined values into an ML model stored in the metering device, which generates a prediction value, which are argued not to be sub-categories of certain methods of organizing human activities that are categorized as abstract. Remarks 04/17/2026 p.11 ¶3 argues that MPEP 2106.04(a)(2) II does not classify the prediction of future commodity usage as fundamental economic activity. In addition, Applicant argues that neither the case law nor the 2019 Guidance identifies a claim being entrepreneurial as evidence that the claim is abstract
Examiner fully considered SME argument 1-organizing human activities but disagrees.
-> First, per Applicant's assertion at Remarks 04/17/2026 p.11 ¶3 that MPEP 2106.04(a)(2) II does not classify the prediction of future commodity usage as fundamental economic activity, the Examiner points to MPEP 2106.04(a) ¶2 which is clear that Examiners are no longer required to use case law, when determining whether the claims recite, describe or set forth the abstract idea: “This approach represents a shift from the former case-comparison approach that required examiners to rely on individual judicial cases when determining whether a claim recites an abstract idea. By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types”. Indeed per, MPEP 2106.04 I, ¶2: “it is sufficient for this analysis for the examiner to identify that the claimed concept (the specific claim limitation(s) that the examiner believes may recite an exception) aligns with at least one judicial exception”. Similarly, per MPEP 2106.04(a) ¶2: “Examiner should determine whether a claim recites an abstract idea by (1) identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and (2) determining whether the identified limitations(s) fall within at least one of the groupings of abstract ideas listed above. The groupings of abstract ideas, and their relationship to the body of judicial precedent, are further discussed in MPEP 2106.04 (a)(2)”. Following such guidelines, and Applicant’s own admission at Remarks 08/16/2025 p.10 ¶2-p.11 ¶1, the Final Act 10/16/2025 p.2 found the claims are directed to predicting usage of a commodity based on the number of users identified at a specific location. Examiner again stresses that, as tested per MPEP 2106.04 I, ¶2 above, predicting “future consumption”, demand or usage of a “commodity”, associated with market participants such as "a user" or consumer and "a utility" [supplier] "supplying the commodity or to" the "user" [or consumer], undeniably falls within the fundamental economic or commercial practices and business relations of MPEP 2106.04(a)(2) II A or B. Indeed, the fact that such “consumption” or usage of “commodity” has to do with market participants such as “utility” [company] “supplying the commodity” and “a number of” [commodity consumers or] “users at the location” (independent Claims 1,11,17) such as at a household read in light of Specification ¶ [0013], corroborates, rather than refutes, the abstract character of the claims. Additional details of the analysis at Final Act 10/16/2025 p.14-p.15 ¶1.
-> Second, and aside from point just one made above, the Examiner submits that, despite the Applicant's allegation to the contrary, there is ample case law to demonstrate that the prediction of future commodity demand is a fundamental principle or practice. For one, in re Bilski and Rand a. Warsaw United States Court of Appeals for the Federal Circuit 2007-1130 (Serial No. 08/833,892) found that the possibility or probability of a spike in demand for coal, [as an example of commodity], is a fundamental principle that necessitates mitigation or hedging. Then, Bilski v. Kappos, 561 U.S. 593,611, 95 USPQ2d 1001, 1010 (2010), as cited by MPEP 2106.04 (a)(II) A ¶ 2, reaffirmed that such a building block of the modern economy remains ineligible. Further still, MPEP 2106.04(a)(II) A ¶ 2, clarified that a fundamental concept, as identified by examiner above, remains abstract and ineligible no matter whether or not it is old or well-known. It follows that here, prediction of future commodity usage as argued by Remarks 04/17/2026 p.11 ¶3 would fall within the abstract scope of the fundamental economic principles or practices (MPEP 2106.04(a)(2) II A) no matter whether or not its implementation using a "metering device" (Claim 1) or "smart meter" (Claims 11, 17) to receive (Claim 1), collect (Claim 11), compile (Claim 17) consumption and consumer or user data, and the transmitting of future consumption (Claims 1,17) would be old or well-known. It would also follow that here no matter whether or not recitations of "wherein the machine learning model is trained based on previously recorded data" and "wherein the machine learning model outputs a forecast" (Claims 1,11,17), in passive diathesis or voice, would be old or well-known, they would still refer to the fundamental and ineligible concept, namely forecasting future consumption of the commodity, as tested per MPEP 2106.04(a)(II) A ¶ 2 and with the underlining claim construction tested per MPEP 2111.04 I.
-> Third, the fact that the claims acquire, collect, compile or receive "by a metering device, data associated with consumption of a commodity by a plurality of consumption devices at a location" at independent Claim 1 and similarly at independent Claims 11,17, does not render said claims less abstract and eligible because the similar "receiving a plurality of data streams, each of the data streams comprising sub-second, time stamped synchronized phasor measurements wherein the measurements in each stream are collected in real time at geographically distinct points over the wide area of the interconnected electric power grid, the wide area comprising at least two elements from among control areas, transmission companies, utilities, regional reliability coordinators, and reliability jurisdictions" did equally not render the claims less abstract and eligible in Electric Power Group, v. ALSTOM S.A., Alstom Grid, Inc., Psymetrix, Ltd., Alstom Limited,. 2015-1778, United States Court of Appeals, Federal Circuit, August 1, 2016".
-> Fourth, and equally important, the fact that the claims consider consumption and consumers or amount of people at the point of consumption or location, as inputs into the machine learning model for subsequent output and transmittal of the forecast of future consumption, does also not render the claims less abstract and eligible because in Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212 (Fed. Cir. 2025), as cited by PTAB Appeal 2025-003304, the use of machine learning to dynamically generate optimized network maps at particular geographical location and times based on [inputted] real-time data and update them based on changing conditions was equally found ineligible with the requirements that the machine learning model be iteratively trained or dynamically adjusted based on real time changes not representing a technological improvement at least because they are incident to the very nature of machine learning. It then follows that here, recitations of "wherein the machine learning model is trained based on previously recorded data associated with the consumption of the commodity by the plurality of consumption devices at the location and monitored by the metering device, and wherein the machine learning model outputs a forecast of future consumption: and transmitting, by the metering device, the forecast of future consumption of the commodity to a utility supplying the commodity or to a user associated with the metering device" at independent Claim 1, and similarly at independent Claims 11,17 would also represent analogous examples where the "machine learning model" would perform functions incident to the very nature of machine learning
Based on the preponderance of legal evidence above, the Examiner finds the Step 2A prong one - Certain Methods of Organizing Human activities unpersuasive.
Step 2A prong 2 Remarks 04/17/2026 p.11 ¶4-p.12 ¶1 argues that similar to Ex Parte Desjardins, the current claimed approach allegedly improves the forecasts generated by a smart meter based on using a machine learning model trained to generate predictions using a combination of consumption data and identification of a number of users present at the specific location as input, citing Original Specification ¶ [0012] at Remarks 04/17/2026 p.12 ¶2, 2nd-3rd sentences. Then at Remarks 04/17/2026 mid-p.12 ¶2, Application argues that the claimed approach involves a metering device executing a trained ML model by inputting both collected consumption data and a number of users to generate a forecast of future consumption for a specific location. citing Figure 3A;,i, and Original Specification ¶ [0051]-¶ [0054]. Thus, it is argued the claimed approach thus improves the accuracy of the consumption prediction for the location. Citing Original Specification ¶ [0011] generating accurate device-level and location-specific predictions as an improvement to the physical infrastructure and not merely as an improvement to an abstract idea itself. Then at Remarks 04/17/2026 p. 13 ¶2, the Applicant points to the language at independent Claim 1 as evidence for applying the abstract idea to the specific practical application of detecting commodity consumption on a per-device level at a location and predicting future consumption of that commodity based at least on that measured consumption and on the monitored number of users present at the location. Finally, at Remarks 04/17/2026 p.13 ¶3, the Applicant argues that these limitations effectuate the improvements to the physical commodity prediction and metering technologies, which historically could not accurately make such predictions, improving the accuracy of consumption forecasting.
Examiner fully considered the Step 2A prong two argument but respectfully disagrees.
With respect to the capabilities of the "metering device" in receiving and then inputting
consumption data of a commodity, the Examiner points to MPEP 2106.05(f)(2)(iii) finding that
a process for monitoring audit log data executed on a computer represents is a mere invocation of computer component or machinery, as tools which does not integrate the abstract exception into a practical application. Similarly, with respect to the involvement of the machine learning as also argued by Remarks 04/17/2026 p.12-p.13 ¶12, Examiner submits that when tested per
MPEP 2111.04 I, the two wherein limitations of "wherein the machine learning model is trained based on previously recorded data associated with the consumption of the commodity by the plurality of consumption devices at the location and monitored by the metering device and wherein the machine learning model outputs a forecast of future consumption" at independent Claim 1 and similarly at independent Claims 11,17 can be argued to have limited patentable weight. This runs complementary to MPEP 2106.04(d)(1) ¶2 warning that if the specification explicitly sets forth an improvement but in a conclusory manner the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. Here, neither the specification nor the claims at the two wherein limitations above, provide anything than a conclusionary manner, namely: "wherein the machine learning model is trained based on previously recorded data associated with the consumption of the commodity by the plurality of consumption devices at the location and monitored by the metering device" "and" "wherein the machine learning model outputs a forecast of future consumption" at independent Claims 1,11,17. Also, the scope of these two limitations refers to the abstract forecasting of consumption, rather than an actual improvement in technology. Thus the claims also lack any components or steps that would allegedly provide any purported technological improvement described in the specification. Yet, Examiner submits in the arguendo, that even if the functionality of the "machine learning" above would be recited in active voice or active diathesis, it would still represent under MPEP 2106.05(f)(2)(i), an example of applying an algorithm for an underlining business process, as mere invocation of a computer component or machinery as tool. This would also not integrate the abstract exception into a practical application.
With respect to Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB September 26, 2025, Appeals Review Panel Decision) (precedential), as raised by Remarks 04/17/2026 p.12 ¶1, the Examiner found that in Desjardins the specification identified the improvement to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” and that the claims reflected the improvement identified in the specification. Indeed, the enumerated improvements identified in the Desjardins specification included disclosures of the effective learning of new tasks in succession in connection with specifically protecting knowledge concerning previously accomplished tasks; allowing the system to reduce use of storage capacity; and the enablement of reduced complexity in the system. Such improvements were tantamount to how the machine learning model itself would function in operation and therefore not subsumed in the identified mathematical calculation.
Here however, the Applicant admits at Remarks 04/17/2026 p.12 ¶2, that the claimed approach improves accuracy of the consumption prediction. As such, such improvement in predicted consumption for a number of users still represents an attempt at improving the abstract exception itself as revealed by Final Act 10/16/2025 p.2-p.11, p.14-p.21 ¶2 and reincorporated herein, with the use of machine learning merely applying such abstract exception without integrating it into a practical application or providing significantly more. Simply put, there is nothing here remotely analogous to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting” to reduce use of storage capacity and the enablement of reduced complexity in the system.
With respect to the allegation made by Remarks 04/17/2026 p.13 ¶3, that the limitations effectuate the improvements to the physical commodity prediction and metering technologies, which historically could not accurately make such predictions, thus improving the accuracy of consumption forecasting, the Examiner points to MPEP 2106.04(a)(2) II A ¶ 2 which states that the term fundamental is not used in the sense of necessarily being old or well-known, but rather as a building block of modern economy. It then follows that making more accurate predictions or forecasting of consumption would remain a fundamental economic practice or principle no matter whether or not historically such predictions of consumption would not have been possible. Further MPEP 2106.04 I ¶3 clarifying that narrow laws that may have limited applications were still held as ineligible. By such tests, the Examiner again resubmits, in the arguendo, that even an argument in favor of limited applications of the “metering device”, “consumption devices”, and “machine learning” in the fundamental, yet not necessarily old or well-known, forecast[ing] “of future consumption of the commodity”, as raised by Applicant at Remarks 04/17/2026 p.13 ¶3 would also not render the claims eligible. Thus, as tested per MPEP 2106.04(a)(2) II A,B, providing more granular or microeconomic, yet still abstract, considerations for “consumption data at a specific location” and census or demographic data for “a number of users at the specific location” do not necessarily render the claims less abstract and eligible. Equally important, an argument in favor of a more accurate prediction of consumption data, as alleged by Applicant, would at most represent an improvement in the abstract, economic, commercial, and/or cognition functions as identified above, through mathematical manipulations, rather than an actual improvement to actual technology. It is critical not to conflate the two especially since MPEP 210.05 I ¶2 states that: "Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract") with MPEP 2106.05 I ¶4 corroborating that a claim for a new abstract idea is still an abstract idea. By such test, it then follows that here, adding the abstract forecast[ing] mathematics as representative here by “using a machine learning model”, to equally abstract considerateness of market participants or consumers set for here as “a number of users at the location” and associated demand or “consumption of a commodity by a plurality of consumption devices at a location” as determin[ed] and receiv[ed] “by” the “metering device” would also not render the claims non-abstract. This finding is once again corroborated by MPEP 2106.05(a) II ¶1 stressing that it “is important to keep in mind that an improvement in the abstract idea itself (e.g. a fundamental economic concept) is not an improvement in technology”. For example, MPEP 2106.05(a) II ¶1 cites Trading Technologies Int’l v IBG,921 F.3d 1084,1093-94,2019 USPQ2d 138290 (Fed Cir. 2019), where the Court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology. Here, similar to Trading Technologies supra, the claims are argued to provide more information represented by “the data associated with the consumption of the commodity, and the number of users at the location” to allegedly improve the business predictive process “of future consumption of the commodity”. As asserted by Applicant above, the benefit or improving, is the accuracy based on consumption data acquired at a specific location and with respect to a number of users at the specific location. Yet, such forecasting improvement is still economic or commercial and thus does not render the claims patent eligible because as initially recognized by Non-Final Act 05/27/2025 p.5 ¶3-p.6 ¶1, an example of performing an analogous computer implemented resampled statistical analysis to generate resampled distribution still represented abstract mathematical calculations in SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018) as cited by MPEP 2106.04(a)(2) I C i. Digging deeper into the Court rationale in SAP supra, the Examiner finds that the alleged problem in SAP was forecasting the behavior of market participants that did not have normal probability distribution, which is analogous here to a consumption distribution problem initially raised by Applicant at Remarks 08/16/2025 p.12 ¶1 as being distinctive consumption patterns at a location due to environmental factors, unique usage habits and occupancy fluctuations. To address is distribution problem the invention in SAP supra focused on resampled statistical analysis using computerized algorithms of boot-strap, jackknife, cross validation, and resampling in the abstract modeling of financial data. Here, similar to the resampled, boot-strap, jackknife, cross validation, the current claims takes into account the “combination of: the data associated with the consumption of the commodity, and the number of users at the location” to later “forecast of future consumption of the commodity”. Yet, the Federal Circuit ruled that “even if one assumes that the techniques claimed are groundbreaking, innovative, or even brilliant those features are not enough for eligibility because their innovation is innovation in ineligible subject matter. An advance of that nature is ineligible for patenting”. It follows that here, even if one assumes, in the arguendo, that taking into account both “the data associated with the consumption of the commodity” receiv[ed] by “metering device” and “number of users at the location” to forecast” “using a machine learning model” “of future consumption of the commodity” would somehow be groundbreaking, innovative, or even brilliant, its use would still be innovation in the ineligible subject matter of commodity forecasting, analogous to the abstract forecasting of financial data without normal distribution as in “SAP” supra. Thus here, as in SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 U.S.P.Q.2d 1638 (Fed. Cir. 2018), “no matter how much of an advance in the field the claims” [would] “recite the advance” [would still] “lie entirely in the realm of abstract ideas” with no plausibly alleged innovation in non-abstract application realm. The SAP findings were corroborated by Versata Dev Grp Inc v SAP Am, Inc 115 USPQ2d 1681 Fed Cir 2015, again undelaying the difference between improvement to an economic goal or objective versus actual improvement to actual technology.
Therefore, the Step 2A prong two argument is unpersuasive.
In conclusion, the Examiner submits that the claims still recite, describe or set forth the abstract exception (Step 2A prong one) with any additional computer-based elements, such machine learning, merely applying the abstract exception as a machinery or tool [MPEP 2106.05(f)] without integrating it into a practical application (Step 2A prong two) or providing significantly more (Step 2B). Thus, the Examiner reasons the claims are still patent ineligible.
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Response to Applicant’s rebuttal arguments on 35 USC 102 and 103
Remarks 04/17/2026 p.15 ¶3 argues Mimaroglu is silent about inputting into a machine learning model a combination of: the data associated with the consumption of the commodity, and the number of users at the location as newly amended at independent Claims 1,11,17
The prior art argument is considered but moot in view of new grounds of rejection.
Examiner now relies on Andrei et al, US 20180285788 A1 to teach or suggests: inputting into a machine learning model a combination of: the data associated with the consumption of the commodity, and the number of users (Andrei ¶ [0006] 1st sentence, ¶ [0041] Fig.2 gives example of input data provided to the prediction algorithm; Annotated Fig.2, ¶ [0072] predictors used in learning comprises consumption value for the property at ¶ [0077] 1st-2nd sentences and also comprises occupancy of number of people living at property per ¶ [0073] and Fig.2, 1st column)
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Andrei Annotated Fig.2 from Original Fig.2 in support of rejection arguments
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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 1-6, 8-14 and 16-20 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, here abstract idea) without significantly more. The claims recite describe, set forth abstract idea of a “commodity” such as “selected from a group consisting of electricity, gas, water, or network bandwidth” (dependent Claims 2,12,19) whose “future consumption” is forecast[ed] based on an input of "a combination of the data associated with the consumption of the commodity and the number of users at the location (independent Claims 1,11,17) with such forecasting further transmit[ed] to “utility supplying the commodity or to a user associated with the metering device” (dependent Claims 1,11,17), and reliant on upon “information regarding weather forecasts; information received from user calendars; a current day of a week; a current date; a current time; data from a solar power generation system located behind the metering device; data from a smart thermostat; and data regarding active and inactive circuits in a smart breaker panel” (dependent Claims 8,14,20). Based on such recitations as read in light of the Background of the Original Specification ¶ [0001]-[0002], it is clear that the claims’ character as a whole is abstract setting forth fundamental economic practices or principles [MPEP 2106.04(a)(2) II A], commercial interactions [MEP 2106.04(a)(2) II B] and/or management of such interactions [MEP 2106.04 (a)(2) II C] that fall within the abstract Certain Methods of Organizing Human Activities grouping, with the term fundamental, as explained by MPEP 2106.04 (a)(2) II A ¶2, not used in the sense of necessarily being old or well-known1 but rather as referring to building blocks of modern economy2. Such building blocks of modern economy are represented here by the “metering” of “consumption of the commodity” with various degree of computerization, ranging from “non-smart consumption devices” to “smart consumption devices” / “meter”. Yet, these narrowing of the abstract exception, does not necessarily render the claims less abstract and eligible since as stated by MPEP 2106.04I ¶3 narrow laws with limited applications are still ineligible3. It follows that here, abstract laws of commodity forecasting, such as "selected from" “electricity, gas, water, and network bandwidth” (Claims 2,12,19) narrowed to a “metering device” (Claims 1,7) / “smart meter” (independent Claims 11,15,17), “non-smart meter” (Claims 3,17), and narrowed to a computerized algorithm for forecasting would not preclude the claims from reciting or, at minimum, describing or setting forth Certain Methods of Organizing Human Activities.
Also, MPEP 2106.04(a)(2) ¶6, 4th sentence is clear that certain activity between a person and computer fall within certain methods of organizing human activity grouping. Thus here, “transmitting the forecast of future consumption of the commodity to a utility supplying the commodity or to a user” (independent Claims 1,11,17) “associated with the metering device” (independent Claim 1) / “the smart meter” (independent Claims 11,17) would also fall within the certain methods of organizing human activity.
Additionally, or alternatively, such fundamental economic practices can also be argued as implementable using mental processes through computer aided cognitive processes and respective mathematical relationships4. For example, MPEP 2106.04(a)(2) III D cites Electric Power Group, 830 F.3d at 1351 and n1 119 USPQ2d at 1740 and n.1, to submit that a wide-area real-time performance monitoring system for monitoring and assessing dynamic stability of an electric power grid was integral an abstract idea. This rationale was later echoed in TDE Petroleum Data Sols., Inc v. AKM Enter., Inc 657 Fed. Appx. 991 (Fed. Cir. 2016), where the Court found determining well operation state as an abstract idea: As we discussed at greater length in Electric Power, the claims of the '812 patent recite the what of the invention, but none of the how that is necessary to turn the abstract idea into a patent-eligible application. Electric Power [2016 BL 247416] 2016 U.S. App. LEXIS 13861 [2016 BL 247416], 2016 WL 4073318 at 4-5. Therefore we find that claim 1 is patent-ineligible under 101. For example, in Electric Power Group, Claim 12 of the '710 patent, required: “receiving a plurality of data streams, each of the data streams comprising sub-second, time stamped synchronized phasor measurements wherein the measurements in each stream are collected in real time at geographically distinct points over the wide area of the interconnected electric power grid, the wide area comprising at least two elements from among control are-as, transmission companies, utilities, regional reliability coordinators, and reliability jurisdictions; receiving data from other power system data sources, the other power system data sources comprising at least one of transmission maps, power plant locations, EMS/SCADA systems; receiving data from a plurality of non-grid data sources”; “detecting and analyzing events in real-time from the plurality of data streams from the wide area based on at least one of limits, sensitivities and rates of change for one or more measurements from the data streams and dynamic stability metrics derived from analysis of the measurements from the data streams including at least one of frequency instability, voltages, power flows, phase angles, damping, and oscillation modes, derived from the phasor measurements and the other power system data sources in which the metrics are indicative of events, grid stress, and/or grid instability, over the wide area”;
Yet, the Federal Circuit ruled that a large portion of the lengthy claims is devoted to enumerating types of information and information sources available within the power-grid environment. But merely selecting information, by content or source, for collection, analysis, and display does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from § 101 undergirds the information-based category of abstract ideas.
Thus here, the similar “receiving” (independent Claim 1) / “collecting” (independent Claim 11) “data associated with consumption of a commodity by a plurality of consumption devices at a location” (independent Claims 1,11) and similarly “compiling data associated with consumption of a commodity by a plurality of smart consumption devices and a plurality of non-smart consumption devices at a location” (independent Claim 17), and the “one or more messages” “identifying the consumption”; “or analysis of data received from respective sensors associated one or more non-smart consumption devices” to “determine” the “consumption” (dependent Claim 4) followed by “transmitting the forecast of future consumption of the commodity to a utility supplying the commodity or to a user” (independent Claims 1,11) “associated with the metering device” (independent Claim 1) / “the smart meter” (independent Claims 11,17), should follow an ineligibility path similar to that of wide-area real-time performance of a monitoring system for monitoring and assessing dynamic stability of an electric power grid as in Electric Power Group and/or determining well operation state as in TDE Petroleum. Also, the subsequent use of “data associated with the consumption of the commodity” “and” “the number of users at the location” for forecast[ing] (Claims 1,6-8,11,13-15,17,20) and “analysis of data received from respective sensors associated with one or more non-smart consumption devices” (dependent Claim 4) could be argued as not meaningfully different than generating 1st and 2nd data by taking existing information, manipulating the data using mathematical correlations, and organizing this information into a new form, found abstract in Digitech Image Techs., LLC v. Electronics for Imaging, Inc., 758 F.3d 1344, 1350, 111 USPQ2d 1717,1721 (Fed Cir. 2014) as cited by MPEP 2106.04(a)(2) I A iv5. Examiner also points to MPEP 2106.04(a)(2) III which found that the combination of computer aided observation, evaluation and judgement to set forth mental processes. For example, MPEP 2106.04 (a)(2) III A cites Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) to state that a claim reciting combination of collecting information, analyzing it, and displaying certain results of the collection and analysis, still set forth the abstract mental processes. It then follows once again that here “receiving” (independent Claim 1) /“collecting” (independent Claim 11) “data associated with consumption of a commodity by a plurality of consumption devices at a location” (independent Claims 1,11) and “compiling data associated with consumption of a commodity by a plurality of smart consumption devices and a plurality of non-smart consumption devices at a location” (independent Claim 17), and the “one or more messages received from one or more smart consumption devices identifying the consumption wherein the one or more smart consumption devices are included in the plurality of consumption devices; or analysis of data received from respective sensors associated with one or more non-smart consumption devices that are included in the plurality of consumption devices” (dependent Claim 4) and “transmitting the forecast of future consumption of the commodity to a utility supplying the commodity or to a user” (independent Claims 1,11) “associated with the metering device” (independent Claim 1) / “the smart meter” (independent Claims 11,17) could be argued as analogous to the abstract forms of computer-aided collected observations presented by MPEP 2106.04 (a)(2) III above, and thus setting forth the abstract exception.
Next, Examiner submits that computer-aided analysis, and/or evaluation and judgement as presented by MPEP 2106.04 (a)(2) III ¶2 do not preclude the claims to recite, describe or set forth the abstract exception. This is becuase, MPEP 2106.04(a)(2) III C. is clear that: # 1. Performing mental process on generic computer, # 2. Performing a mental process in a computer environment, and # 3. Using a computer as a tool to perform a mental process, do not preclude the claims from reciting the abstract idea. It then follows that here, given its high level of generality, the “machine learning model” as in "wherein the machine learning model is trained based on previously recorded data associated with the consumption of the commodity by the plurality of consumption devices at the location and monitored by the metering device, and wherein the machine learning model outputs a forecast of future consumption" could perhaps be argued, along with the associated “metering device” / “smart meter” and “consumption devices” as a computer tool and/or computer environment upon which the above abstract processes are being performed. Further, the fact that the “machine learning model is trained based on previously recorded data associated with the consumption of the commodity” (Claims 1,11,17) does not necessarily render the claims non-abstract, because according to MPEP 2106.04(a)(2) II C ii. considering historical usage information while inputting data6, still sets forth the abstract exception.
It is perhaps worth mentioning that MPEP 2106.04(a)(2) I C i. cites SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), to state that performing resampled statistical analysis to generate resampled distribution represents abstract mathematical calculations. Digging deeper into the Court rationale in SAP supra, the Examiner finds that its invention focused on resampled statistical analysis using computerized algorithms of boot-strap, jackknife, cross validation, and resampling in the abstract modeling of financial data. Yet, the Federal Circuit ruled that “even if one assumes that the techniques claimed are groundbreaking, innovative, or even brilliant those features are not enough for eligibility because their innovation is innovation in ineligible subject matter. An advance of that nature is ineligible for patenting”. Thus here, even if one assumes, in the arguendo, that the input of both
"the data associated with the consumption of the commodity, and the number of users at the location" into “machine learning model” for subsequent output of "forecast of future consumption"
(independent Claims 1,11,17) would somehow be groundbreaking, innovative, or even brilliant, its use would still be innovation in the ineligible subject matter of commodity forecasting, as analogous to the abstract financial data of “SAP” supra. Thus here, as in SAP Am., Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 U.S.P.Q.2d 1638 (Fed. Cir. 2018), “no matter how much of an advance in the field the claims” [would] “recite the advance” [would still] “lie entirely in the realm of abstract ideas” with no plausibly alleged innovation in non-abstract application realm. In an abundance of caution, the claimed computerization, including use of “machine learning model” will be more granularly tested at subsequent steps below.
For now, it is clear that, given the preponderance of legal evidence above, the character as a whole of the claims remains undeniably abstract as setting forth one or more of certain methods of organizing human activities [MPEP 2106.04(a)(2) II), mental processes [MPEP 2106.04(a)(2) III] and/or mathematical concepts [MPEP 2106.04(a)(2) I]. Step 2A prong one.
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This judicial exception is not integrated into a practical application because per Step 2A prong two, the individual or combination of the additional, computer-based elements is/are found to merely apply the already recited abstract idea. Here, if not already computer aids as tested above, the computer-based components would at most qualify as additional elements such as: “the machine learning model” (Claims 1,5,6,11,13,14,17), “non-smart consumption devices” (Claims 3,4,17) and “metering device” (Claims 1,5,8-9) as well as “smart meter” (Claims 11,14,16, 17-20) and associated “user devices” “connected to” “Wi-Fi network”, “Bluetooth connection”, or “GPS information” recited in alternative (Claims 9,10,16,18), and arguably the associated “data from a smart thermostat; and data regarding active and inactive circuits in a smart breaker panel” (dependent Claims 8,14,20). Here, even when tested per MPEP 2106.05(f)(2)(i), such elements would represent mere computer components used or invoked as tools, upon which the business process of commodity forecasting as identified above, is being applied, using the identified “machine learning model” or algorithm at Claims 1,5,6,8,11,13,14,17,20, which as revealed by MPEP 2106.05(f)(2)(i) does not integrate the abstract idea into a practical application. Also, MPEP 2106.05(f)(2) ¶1 is clear that use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) does not integrate a judicial exception into a practical application. It then follows that here use of computer elements such as “metering device”, “smart” and “non-smart” meters as revealed by “receiving” (independent Claim 1) /“collecting” (independent Claim 11) “by a metering device, data associated with consumption of a commodity by a plurality of consumption devices at a location” (independent Claims 1,11) and “compiling data associated with consumption of a commodity by a plurality of smart consumption devices and a plurality of non-smart consumption devices at a location” (independent Claim 17), and the “one or more messages received from one or more smart consumption devices identifying the consumption wherein the one or more smart consumption devices are included in the plurality of consumption devices; or analysis of data received from respective sensors associated with one or more non-smart consumption devices that are included in the plurality of consumption devices” (dependent Claim 4), “user devices” “connected to” “Wi-Fi network”, “Bluetooth connection”, or “GPS information” recited in the alternative (Claims 9,10,16,18) and “transmitting the forecast of future consumption of the commodity to a utility supplying the commodity or to a user” (independent Claims 1,11,17) “associated with the metering device” (independent Claim 1) / “the smart meter” (independent Claims 11,17) would also not integrate the abstract exception into a practical application. Similarly, MPEP 2106.05(f)(2)(iii),(v) states that a process for monitoring audit log data executed on general-purpose computer, and a process of requiring use of software to tailor information and provide it to the user on a generic computer, do not integrate the abstract exception into a practical application. It then follows that here, use of “non-smart consumption devices” (Claims 3,4,17) and “metering device” (Claims 1,5,8-9) as well as “smart meter” (Claims 11,14,16, and Claims 17-20) and associated “Wi-Fi network”, “Bluetooth connection”, and “GPS information” recited in the alternative at Claims 9,10,16,18, to monitor commodity consumption, and the subsequent transmitting of forecasted future consumption, would similarly not integrate the abstract exception into a practical application. Further the fact that the “the machine learning model” is being train[ed] “prior to installation of the machine learning model in the metering device” (dependent Claim 5) could equally be argued as an example of generality of the application of the judicial exception, which according to MPEP 2106.05(f)(3) does not integrate the abstract idea into a practical application. Further MPEP 2106.05(h) vi. cites Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016), to state that limiting the combination of collecting, analyzing and displaying certain results of the collection and analysis, to a data related to technological environment or field of use does not integrate the abstract idea into a practical application. It then follows that here, narrowing the aforementioned collecting, analyzing and displaying certain results of the collection and analysis to data related to “non-smart consumption devices” at Claims 3,4,17 and “metering device” at Claims 1,5,8-9 as well as the “smart meter” at Claims 11,14,16, and Claims 17-20 and associated “Wi-Fi network”, “Bluetooth connection”, and “GPS information” recited in the alternative at Claims 9,10,16,18, and the field of use at dependent Claim 5, would also not integrate the abstract idea into a practical application.
Thus here, there is a preponderance of legal evidence for the claims not reciting additional, computer-based elements capable to integrate the abstract exception into a practical application.
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The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as shown above, the additional computer-based elements merely apply the already recited abstract idea and link the use of abstract idea to a field of use or technological environment. Examiner follows MPEP 2106.05 (d) II and carries over the findings tested per MPEP 2106.05 (f),(h) to submit that the additional computer-based elements also do not provide significantly more. Even assuming arguendo, that further evidence would be required to demonstrate conventionality of the additional, computer-based elements, Examiner would also point as evidence to the high level of generality of the additional elements read in light of Original Disclosure:
- Original Specification ¶ [0013] 3rd sentence reciting at high level of generality: “the disclosed techniques leverage consumption patterns and user data to train a machine learning model within the metering device”
- Original Specification ¶ [0034] 5th sentence reciting at high level of generality: “Smart thermostats are typically equipped with Wi-Fi or other connectivity options, enabling users to remotely monitor and adjust temperature settings via a user device 204”.
- Original Specification ¶ [0039] 5th sentence: “In some embodiment, the smart meter 160 can also be configured to detect user devices 204 and activity using the Bluetooth or Global Positioning System (GPS) connections associated with the user devices 204”.
- Original Specification ¶ [0065] reciting at high level of generality “Smart meter 160 is a network device and includes computing device hardware configured to perform various processing operations and execute program code. The smart meter 160 can further include various analog-to-digital and digital-to-analog converters, digital signal processors (DSPs), harmonic oscillators, transceivers, and any other components generally associated with RF-based communication hardware. In various embodiments, smart meter 160 includes a battery (not shown) that supplies power to the various computing device hardware included in smart meter 160”.
- Original Specification ¶ [0107] 5th sentence reciting at high level of generality: “Such processors may be, without limitation, general purpose processors, special-purpose processors, application-specific processors, or field-programmable gate arrays”.
If necessary, Examiner would also point to MOPEP 2106.05(d) II i. showing the conventionality of receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). It then follows that here “user devices” “connected to” “Wi-Fi network”, “Bluetooth connection”, or “GPS information” recited in the alternative (Claims 9,10,16,18) and “transmitting the forecast of future consumption of the commodity to a utility supplying the commodity or to a user” (Claims 1,11) “associated with the metering device” (Claim 1) / “the smart meter” (Claims 11,17) could also be argued as conventional. Similarly, MPEP 2106.05(d) II ii finds performing repetitive calculations (Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278,103 USPQ2d 1425,1433 (Fed Cir 2012) are well‐understood, routine, and conventional functions. It then follows that here “updating the machine learning model based on differences between the forecast of future consumption of the commodity and an actual future consumption of the commodity by the plurality of consumption devices at the location” (Claims 6,13) would also represent examples of performing repetitive calculations incapable to provide significantly more.
In conclusion Claims 1-6, 8-14 and 16-20 although directed to statutory categories (“method” or process at Claims 1-6, 8-10, “non-transitory storage medium”, or article of manufacture at Claims 11-14,16, “smart meter” or machine Claims 17-20 ) they still recite or set forth the abstract idea (Step 2A prong one), with their additional, computer based elements not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more than the abstract idea itself (Step 2B).
Thus, the Claims 1-6, 8-14, 16-20 are patent ineligible.
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Rejections under 35 § U.S.C. 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 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1,2,8,11,12 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over:
Andrei et al, US 20180285788 A1 hereinafter Andrei, in view of
Henze et al, US 20240219549 A1 hereinafter Henze. As per,
Claims 1,11 Andrei teaches “A method comprising/One or more non-transitory computer-readable media storing instructions which, when executed by one or more processors of a smart meter, cause the one or more processors to perform operations” (¶ Andrei ¶ [0151]) comprising”
- “receiving” (Clam 1) / “collecting” (Claim 11) “by a metering device, data associated with consumption of a commodity by a plurality of consumption devices at a location” (Andrei ¶ [0056], ¶ [0151] 2nd sentence energy consumption data obtained from smart meters1502 installed in residential propertiesto provide at ¶ [0010], ¶ [0100] a breakdown of energy usage broken down into: heating, hot water, cooking (e.g. electric ovens, gas hobs etc.), large appliances (e.g. fridges, washing machines etc.), entertainment (e.g. TVs, games consoles etc.), lighting. ¶ [0029] 5th sentence: the invention may also be more broadly applied to other contexts and forms of energy consumers (e.g. energy consumers could represent individual appliances in a property, individual machines or production lines in a factory, individual vehicles in a vehicle fleet, and the like).
- “determining // a number of users at the location” (Claims 1,11) (Andrei ¶ [0029] 5th sentence: energy consumers correspond to customers of an energy supplier (e.g. particular residential property will be associated with a particular energy customer, who is generally an occupant of the property. ¶ [0017] the attribute values… for the energy consumer comprise… occupancy attribute indicating a number of occupants of the property) “and”
- “inputting by the metering device into a machine learning model a combination of: the data associated with the consumption of the commodity and the number of users at the location” (Claims 1,11) (Andrei ¶ [0006] 1st sentence, ¶ [0041] Fig.2 gives example of input data provided to the prediction algorithm; Annotated Fig.2, ¶ [0072] predictors used in learning comprises consumption value for property at ¶ [0077] 1st-2nd sentences and comprises occupancy of number of people living at property per ¶ [0073], Fig.2, 1st column)
“wherein the machine learning model is trained based on previously recorded data associated with the consumption of the commodity by the plurality of consumption devices at the location” (Andrei ¶ [0027] 1st,3rd sentences: each energy consumption profile corresponds to a given time period and comprises a time series of energy consumption data values, each data value relating to consumption during a respective time interval within the given time period (i.e. day, week, month, year). ¶ [0072] predictors used in the training are exemplified at ¶ [0077] 1st-2nd sentences as consumption value (i.e. daily average consumption) for the property, in units of kWh. ¶ [0091] 1st-2nd sentences: input data provide consumption data at a given temporal resolution, for example daily, monthly or yearly. The analysis process may be repeated for different time periods. Fig.7 and ¶ [0100] The above techniques may be adapted to provide a predicted breakdown of energy usage into a defined set of consumption categories. In one embodiment, energy usage is broken down into categories: large Appliances (e.g fridges, washing machines etc.) entertainment (e.g. TVs, games consoles etc.), lighting) “and monitored by the metering device” (Claim 1) / “smart meter” (Claim 11) (Andrei ¶ [0151] 2nd sentence noting energy consumption data is obtained from smart meter 1502) “and”
“wherein the machine learning model outputs a forecast of future consumption" "and"
(Andrei ¶ [0151] 1st-3rd sentences: The system includes an analysis server 1500 for performing data analysis. Energy consumption data is obtained from smart meters 1502 installed in residential properties by a collection system 1504. The consumption data is augmented with property attribute data (i.e. number of occupants of the property exemplified at ¶ [0017]) for use as predictive [or forecast] features in the learning algorithms. Specifically per, ¶ [0020] generating predicted energy consumption data for each of a plurality of time periods and/or for each of fuel types, optionally by repeating the model generating and prediction determining steps using respective sets of training samples for each of the time periods (e.g. days, weeks months etc.) and/or fuel types (e.g. gas and electricity).
- “transmitting, by the metering device, the forecast of future consumption of the commodity to a utility supplying the commodity” (Andrei ¶ [0151] 2nd, 5th sentences: energy consumption from smart meters 1502 is used for billing purposes, by the energy provider of ¶ [0152] 1st sentence) “or to a user associated with the metering device” (Andrei ¶ [0028] each energy consumption profile is preferably associated with a respective energy consumer. ¶ [0122] 1st -2nd sentences: An example of an output screen is shown in Fig.9. The output shows a consumption value for each of the categories based on the output of the breakdown prediction.
¶ [0123] The display also shows (on the right, Your home profile) the attribute values entered for the target property, which are used as predictive features by the prediction algorithm).
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Andrei Annotated Fig.2 from Original Fig.2 in support of rejection arguments
* While *
Andrei above teaches “determining” “a number of users at the location”
Andrei does not explicitly recite to clearly anticipate that “metering device” (Claim 1) or “smart meter” (Claim 11) “determining”, “number of users at the location” (Claims 1,11) as claimed.
Andrei also might or might not suggest but certainly does not explicitly recite: “machine learning model stored in a memory of the metering device”/“smart meter” (claims 1,11) as claimed.
* However *
Henze in analogous monitoring energy consumption (Henze ¶ [0010] 2nd sentence, ¶ [0026] second sentence) teaches or at least suggests: “metering device” (Claim 1) or “smart meter” (Claim 11) is “determining”, “a number of users at the location” (Claims 1,11) and “machine learning model stored in a memory of the metering device” (claims 1,11)
(Henze ¶ [0094] … a storage that stores a machine-learned AI model for inferring likelihood of human occupancy… For example, at ¶ [0029] 1st-2nd sentences: sensing system 100 comprises base station unit 110, which, at ¶ [0035] is configured to monitor the electric distribution system within a building to provide an additional signal about human activity. For example, when a user activates an electrical device, such as a vacuum cleaner, within a household, the electrical device introduce an electromagnetic interference signal within the electric distribution system of the building. The base station 110 [of sensing system 100] comprise a set of stored electromagnetic interference signal fingerprints within its computer storage media 114. The base station 110 utilize processors 112 and the fingerprints and/or a neural network [as example of machine learning] to map the electromagnetic interference signal to human activity. Further, in some embodiments, the base station 110 maps the electromagnetic interference signal to a particular appliance or electrical device using the electromagnetic interference signal fingerprints and/or the neural network. Accordingly, the system 100 is able to more accurately infer the likelihood of human occupancy based on the electromagnetic interference signal).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Andrei’s "method" / "non-transitory media" to have included Henze’s teachings or suggestions in order to have provided improve detection of household occupancy (Henze ¶ [0004]- ¶ [0006], in view of MPEP 2143 G) using a rigorous neural network algorithm (Henze ¶ [0035] in view of MPEP 2143 G). The predictability of such modification would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Andrei ¶ [0158]-¶ [0161] in view of Henze ¶ [0085] - ¶ [0086].
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with energy consumption and associated household occupancy. In such combination each element would have merely performed the same analytical, benchmarking, econometric and managerial function as separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Andrei in view of Henze, the to be combined elements would have fitted together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the combination results would have been predictable (MPEP 2143 A).
Claims 2,12 Andrei / Henze teaches all the limitations in claims 1,11 above. Further,
Andrei teaches or suggests “commodity is selected from a group7 consisting of: electricity, gas, water” (Andrei ¶ [0006] 3rd-4th sentences: consumption values and/or predicted energy consumption value may specify a total energy consumption value, optionally for a given fuel type used by an energy consumer (e.g. gas/electricity) and/or for a given time period. Any suitable measure may be used to represent energy/fuel consumption, such kWh, m3 (e.g. for gas)), and network bandwidth”
Claims 8,14 Andrei / Henze teaches all the limitations in claims 1,11 above. Furthermore,
Andrei teaches wherein the combination the further includes other data relevant to the consumption of the commodity selected from a group8 consisting of / wherein the machine learning model is further trained based on other data relevant to the consumption of the commodity selected from a group consisting of:
- “information regarding weather forecasts”;
- “information received from user calendars”;
- “a current day of a week”; (Andrei ¶ [0146] 2nd sentence: data for Monday-Friday may be combined and clustered to produce a clustering indicative of weekday behaviour patterns, and data for weekend days combined to produce a clustering indicative of weekend behavior patterns)
- “a current date” (Andrei Fig.4 and ¶ [0148] 2nd sentence: determine behaviour patterns over a longer period, for example to generate a single clustering for July 31st, 2015);
- a current time; (Andrei ¶ [0135] 1st sentence: the time series data consists of half-hourly energy consumption values over a given time period i.e. day, a week, a month or any time period).
- “data from a solar power generation system located behind the metering device”;
- “data from a smart thermostat”; “and”
- “data regarding active and inactive circuits in a smart breaker panel”.
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Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over:
Andrei / Henze as applied to claim 1 above, view of
Ambati et al, US 20130046703 A1, by Applicant IBM, hereinafter Ambati. As per,
Claim 3. Andrei / Henze teaches all the limitations in claim 1. Furthermore,
Andrei / Henze does not explicitly recite “the plurality of consumption devices includes any combination of non-smart consumption devices and smart consumption devices as claimed.
Ambati however in analogous communications for power consumption information teaches/suggests: “the plurality of consumption devices includes any combination of non-smart consumption devices and smart consumption devices” (Ambati ¶ [0025] 2nd-3rd sentences: If a customer has smart appliances for some devices, the invention can find the share of smart devices in his/her total consumption. If the remaining consumption (due to non-smart devices) is above a threshold, the presence of power-consuming devices such as a refrigerator or AC that are not smart appliances can be inferred).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Andrei/Henze’s “method” to have included Ambati’s teachings or suggestion to have provided better recommendations for smart appliances for these commonly used utilities and the corresponding potential savings achievable by adopting those appliances (Ambati ¶ [0025] 4th sentence in view of MPEP 2143 G and/or F). The predictability of such modification would have been corroborated by the broad knowledge of one of ordinary skills in the art as articulated by Andrei ¶ [0158]-¶ [0161] in view of Henze ¶ [0085]-¶ [0086] in further view of Ambati ¶ [0037], ¶ [0054], ¶ [0056], ¶ [0058].
Additionally, or alternatively, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with the gathering and analysis of power consumption. In such combination each element merely would have performed the same consumption data gathering and analysis function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Andrei / Henze in further view of Ambati, the to be combined elements would have fitted together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Claim 4 Andrei/Henze teaches all the limitations in claim 1 above.
Andrei ¶ [0151] 2nd sentence recites: Energy consumption data is obtained from smart meters 1502 installed in residential properties by a collection system 1504. Nevertheless,
Andrei/Henze does not teach “the data associated with the consumption is based on”
- “one or more messages received from one or more smart consumption devices identifying the consumption wherein the one or more smart consumption devices are included in the plurality of consumption devices”; “or”
- “analysis of data received from respective sensors associated with one or more non-smart consumption devices that are included on the plurality of consumption devices” as claimed.
Ambati however in analogous communications for power consumption information teaches/suggests “the data associated with the consumption is based on”:
- “one or more messages received from one or more smart consumption devices identifying the consumption wherein the one or more smart consumption devices are included in the plurality of consumption devices”; (Ambati ¶ [0016] 2nd-5th sentences: For a particular home, the list is addressed by indexing into the array Appliance Installed [Home Instance]. The smart appliances typically installed in homes with the same profile as the home under consideration is held in an array of appliance profiles (ApplianceProfile). For a particular profile, the list is addressed by indexing into array ApplianceProfile [ProfileInstance]. When the power consumption for a particular home is over the threshold for homes with that profile, the list of appliances for homes with that profile is retrieved. ¶ [0024] Bill presentation with alerts is also included in the invention. According to preferences set by the user, an aspect of the invention can include sending alerts as and when certain events are observed. For example, once a user's savings result in reducing one ton of CO2, a congratulatory alert can be sent out. For example, at ¶ [0025] 2nd sentences: If a customer has smart appliances for some devices, the invention can find the share of smart devices in his/her total consumption) “or”
- “analysis of data received from respective sensors associated with one or more non-smart consumption devices that are included on the plurality of consumption devices”
(Ambati ¶ [0025] 3rd sentences: If remaining consumption (due to non-smart devices) is above a threshold, the presence of power-consuming devices such as refrigerator or AC that are not smart appliances can be inferred)
Rationales to have modified/combined Andrei/Henze/Ambati are above and reincorporated.
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Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over:
Andrei/Henze as applied to claim 1 above, in view of
Cheim; Luiz US 20230420938 A1 hereinafter Cheim. As per,
Claim 5 Andrei/Henze teaches all the limitations in claims 1. Furthermore,
Andrei/Henze does not explicitly recite “further comprising”
- “training, based on previously recorded consumption data, the machine learning model prior to installation of the machine learning model in the metering device”. However,
Cheim in analogous art of predicting load parameter value of electrical equipment teaches/suggests:
- “training, based on previously recorded consumption data, the machine learning model prior to installation of the machine learning model in the metering device”
(Cheim ¶ [0046] 1st sentence training and selection of machine learning models to predict future loads for electrical equipment based on historical load data. ¶ [0058] In some examples, the at least one machine learning model is trained based on plurality of determined relationships between a predefined number of load parameter values and at least one subsequent load parameter value from a time series data stream obtained for a predetermined period of time. In this example, training is performed before deploying [or installing] the machine learning model).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Andrei/Henze’s “method” to have included Cheim’s teachings or suggestions to have advantageously used Cheim’s machine learning techniques to have provided very high accurate prediction of future consumption based on relatively small single variable datasets of historical load over time, without need for other external parameters (Cheim ¶ [0043] in view of MPEP 2143 G) The predictability of such modification would have been further corroborated by the broad knowledge of one of ordinary skills in the art as articulated by Andrei ¶ [0158]-¶ [0161] in view of Henze ¶ [0085]-¶ [0086] in further view of Cheim ¶ [0034], ¶ [0073].
Additionally, or alternatively, the claimed invention could have also been viewed as a mere combination of old analytical elements in a similar field of endeavor of predicting load or consumption parameters. In such combination each element would have merely performed same data processing and analytical function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Andrei/Henze in further view of Cheim, the to be combined elements would have fitted together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
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Claims 6,13 are rejected under 35 U.S.C. 103 as being unpatentable over:
Andrei/Henze as applied to claims 1,11 above, in further view of
Tejima; Go US 20230035501 A1 hereinafter Tejima. As per,
Claims 6,13 Andrei/Henze teaches all the limitations in claims 1,11 above. Further,
Andrei teaches “updating”…“future consumption of the commodity by the plurality of consumption devices at the location” (Andrei ¶ [0056] the analysis techniques are applied to energy consumption by residential energy users, and consumption is measured and analyzed at the level of individual households or properties, where a household/property typically corresponds to a residential property such as a house or apartment. Each such property typically corresponds to a customer of an energy supplier. ¶ [0147] the algorithm may be run repeatedly on time series data for respective different time periods, and the clustering results obtained for different time periods may then be combined subsequently. ¶ [0148] For example, data for individual specific days may be clustered to determine day-specific behaviour clusters (e.g. a clustering could be performed separately for each day from 1 July to 31 July 2015). The resulting clustering for individual dates may then be combined to determine behaviour patterns over a longer period (for example to generate a single clustering for July 2015). In one approach, this may be done by performing a second level of clustering, using a similar algorithm as described above, to cluster individual day behaviour patterns into behaviour patterns representative of behavioural variations and patterns over a longer period (e.g. a month). However,
Andrei/Henze does not explicitly recite to clearly anticipate: “further comprising”
- “updating the machine learning model based on differences between the forecast of future consumption of the commodity and an actual future consumption of the commodity”… Yet
Tejima in analogous monitoring demand of energy consumption teaches/suggest
- “updating the machine learning model based on differences between the forecast of future consumption of the commodity and an actual future consumption of the commodity”.
(Tejima Fig.13, steps S120-> S122 and ¶ [0136] The training continuation processing section 160 determines whether or not the difference between a predicted value of demand and an actual result value is a predetermined threshold value or greater, as in S120 in Fig.13 (step S221). When it is determined that the difference between the predicted value of demand and the actual result value is the predetermined threshold value or greater (YES in S221), the training continuation processing section 160 performs training continuation processing, as in S122 in Fig.13 (step S222). When it is not determined that the difference between the predicted value of demand and the actual result value is the predetermined threshold value or greater (NO in S221), the process in S222 is not performed. As described above, since demand for hydrogen changing according to a determined hydrogen price can be predicted, accuracy of prediction of demand for hydrogen can be further enhanced. Then, the processes in S212 to S222 can be repeated).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Andrei/Henze method/non-transitory computer-readable media to have included Tejima’s teachings to have more rigorously enhanced the prediction model accuracy corresponding to a demand change such as a demand increase (Tejima ¶ [0092], ¶ [0106], ¶ [0109] in view of MPEP 2143 G and/or F). Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar modeling or forecasting commodity consumption field of endeavor. In such combination, each element would have merely performed same iterative analytical and modeling function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Andrei/Henze in further view of Tejima, the to be combined elements would have fitted together like pieces of a puzzle to achieve “updating the machine learning model based on differences between the forecast of future consumption of the commodity and an actual future consumption of the commodity by the plurality of consumption devices at the location” in a manner that it would have been logical complementary, technologically feasible and/or economically desirable. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
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Claims 9,10 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over:
Andrei/Henze as applied to claims 1,11 above, in view of
Krishnaswamy et al, US 20240411353 A1 hereinafter Krishnaswamy.
Claim 9, Claim 16 (1st half) Andrei/Henze teaches all the limitations in claims 1,11 above.
Andrei/Henze does not explicitly recite: determining the number of users at the location comprises determining a number of user devices, associated with the users, that are connected to a Wi-Fi network that is in communication with the metering device” / “smart meter” as explicitly claimed. Yet,
Krishnaswamy in analogous smart energy management teaches/ suggests: “determining a number of user devices, associated with the users, that are connected to a Wi-Fi network that is in communication with the metering device” / “smart meter” (Krishnaswamy ¶ [0080] last 2 sentences: 1st computing device 104 may be aggregate node for example smart meter that aggregates energy info across different consuming devices such as the entities (102) in a home or an enterprise. In particular, 1st computing device (104) is implemented as smart meter. ¶ [0081] 1st-2nd sentences: In an embodiment, the one or more sensors may send the collected information to a local aggregate node such as a first computing device (104), which may forward the information to the system (110) for further analysis. In an example embodiment, the one or more sensors may communicate with the local aggregate node over suitable protocols including, but not limited to, Wireless Fidelity (Wi-Fi), Bluetooth, Zigbee, cellular 4G/5G/6G device to device communications, and the like).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Andrei/Henze method/non-transitory computer-readable media to have further included Krishnaswamy’s teachings to have allowed the users to be equipped with the necessary information regarding their energy usage, to have empowered them to act effectively on the basis of that information for reviewing and acting upon the available information in an effective manner (Krishnaswamy ¶ [00065]- ¶ [0007], ¶ [0055] in view of MPEP 2143 G and/or F) while, at the same time, further optimizing the delivery of energy in the most efficient way (Krishnaswamy ¶ [0089] 4th sentence in view of MPEP 2143 G and/or F). The predictability of such modification would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Andrei ¶ [0158]-¶ [0161] in view of Henze ¶ [0085]-¶ [0086] and in further view of Krishnaswamy ¶ [0120].
Additionally, or alternatively, the claimed invention could have also been viewed as a mere combination of old elements in a similar smart energy management field of endeavor. In such combination each element merely would have performed same analytical and data processing function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Andrei/Henze in further view of Krishnaswamy, the to be combined elements would have fitted together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Claim 10, Claim 16(2nd half) Andrei/Henze teaches all the limitations in claims 1,11 above.
Andrei/Henze is not determining the number of users at the location
comprises determining a number of user devices, associated with the users that are using an active Bluetooth connection on the user devices, or using GPS information from the user devices”
Krishnaswamy in analogous smart energy management teaches:
- “determining a number of user devices, associated with the users that are using an active Bluetooth connection on the user devices, or using GPS information from the user devices” (Krishnaswamy ¶ [0080] last two sentences: first computing device (104) may be aggregate node, for example, smart meter that aggregates energy information across different consuming devices such as the entities (102) in a home or an enterprise. In particular, the first computing device (104) may be implemented as a smart meter in an exemplary embodiment. ¶ [0081] 1st-2nd sentences: In an embodiment, the sensors may send the collected information to a local aggregate node such as 1st computing device (104), which may forward the information to the system (110) for further analysis. In an example embodiment, the one or more sensors may communicate with the local aggregate node over suitable protocols including, but not limited to, Wireless Fidelity (Wi-Fi), Bluetooth, Zigbee, cellular 4G/5G/6G device to device communications, and the like).
Rationales to have modified/combined Andrei/Henze/Krishnaswamy are above & reincorporated.
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Claims 17, 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over:
Andrei et al, US 20180285788 A1 hereinafter Andrei, in view of
Henze et al, US 20240219549 A1 hereinafter Henze in view of
Ambati et al, US 20130046703 A1, by Applicant IBM, hereinafter Ambati. As per,
Claim 17 Andrei teaches “A smart meter, comprising: one or more processors; and a memory storing executable instructions that, when executed by the one or more processors, cause the one or more processors" (Andrei ¶ [0151]-¶ [0161]) "to”:
- “compiling data associated with consumption of a commodity by a plurality of smart consumption devices”;
(Andrei ¶ [0056], ¶ [0151] 2nd sentence energy consumption data obtained from smart meters1502 installed in residential propertiesto provide at ¶ [0010], ¶ [0100] a breakdown of energy usage broken down into: heating, hot water, cooking (e.g. electric ovens, gas hobs), large appliances (e.g. fridges, washing machines etc.), entertainment (e.g. TVs, games consoles etc.), lighting. ¶ [0029] 5th sentence: the invention may also be more broadly applied to other contexts and other forms of energy consumers (e.g. energy consumers could represent individual appliances in a property, individual machines or production lines in a factory, individual vehicles in a vehicle fleet, and the like).
- “determining a number of users at the location based on a number of user devices
(Andrei ¶ [0029] 5th sentence: energy consumers correspond to customers of an energy supplier (e.g. particular residential property will be associated with a particular energy customer, who is generally an occupant of the property. ¶ [0017] the attribute values… for the energy consumer comprise… occupancy attribute indicating a number of occupants of the property) “and”
- “inputting, into a machine learning model a combination of: the data associated with the consumption of the commodity, and the number of users at the location” (Andrei ¶ [0006] 1st sentence, ¶ [0041] Fig.2 gives example of input data provided to the prediction algorithm. Annotated Fig.2 below ¶ [0072] predictors used in learning comprises consumption value for the property at ¶ [0077] 1st-2nd sentences and also comprises occupancy of the number of people living at the property per ¶ [0073] and Fig.2, 1st column), “wherein the machine learning model is trained based on previously recorded data associated with the consumption of the commodity by the plurality of consumption devices at the location (Andrei ¶ [0027] 1st,3rd sentences: each energy consumption profile corresponds to a given time period and comprises a time series of energy consumption data values, each data value relating to consumption during a respective time interval within the given time period (i.e. day, week, month, year). ¶ [0072] predictors used in the training are exemplified at ¶ [0077] 1st-2nd sentences as consumption value (i.e. daily average consumption) for the property, in units of kWh. Fig.7 and ¶ [0100] The above techniques may be adapted to provide a predicted breakdown of energy usage into a defined set of consumption categories. In one embodiment, energy usage is broken down into categories: Large Appliances (e.g. fridges, washing machines etc.), Entertainment (e.g. TVs, games consoles etc.) Lighting) “and monitored by the smart meter” (Andrei ¶ [0151] 2nd sentence noting energy consumption data is obtained from smart meter 1502) “and” “wherein the machine learning model outputs a forecast of future consumption” (Andrei ¶ [0151] 1st-3rd sentences: The system includes an analysis server 1500 for performing data analysis. Energy consumption data is obtained from smart meters 1502 installed in residential properties by a collection system 1504. The consumption data is augmented with property attribute data (i.e. number of occupants of the property exemplified at
¶ [0017] for use as predictive [or forecast] features in the learning algorithms. Specifically per,
¶ [0020] generating predicted energy consumption data for each of a plurality of time periods and/or for each of fuel types, optionally by repeating the model generating and prediction determining steps using respective sets of training samples for each of the time periods (e.g. days, weeks months etc.) and/or fuel types (e.g. gas and electricity); “and”
- “transmitting the forecast of future consumption of the commodity to a utility supplying the commodity” (Andrei ¶ [0151] 2nd,5th sentence: energy consumption from smart meters 1502 is used for billing purposes, by the energy provider of ¶ [0152] 1st sentence) “or to a user associated with the smart meter” (Andrei ¶ [0028] each energy consumption profile is preferably associated with a respective energy consumer. ¶ [0122] 1st -2nd sentences: An example of an output screen is shown in Fig.9. The output shows a consumption value for each of the categories based on the output of the breakdown prediction. ¶ [0123] The display also shows (on the right, Your home profile) the attribute values entered for the target property, which are used as predictive features by the prediction algorithm).
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Andrei Annotated Fig.2 from Original Fig.2 in support of rejection arguments
* While *
Andrei above teaches “determining” “a number of users at the location”
Andrei does not explicitly recite to clearly anticipate “a wireless connection” as explicitly required by "determining a number of users at the location based on a number of user devices with a wireless connection at the location” as explicitly claimed.
Andrei also might or might not suggest but certainly does not explicitly recite to anticipate: “machine learning model stored in a memory of the metering device” as explicitly claimed.
* However *
Henze in analogous monitoring energy consumption (Henze ¶ [0010] 2nd sentence, ¶ [0026] second sentence) teaches or suggests: “machine learning model stored in a memory of the metering device” (Henze ¶ [0094] … a storage that stores a machine-learned AI model for inferring likelihood of human occupancy… For example at ¶ [0029] 1st-2nd sentences: sensing system 100 comprises base station unit 110, which, at ¶ [0035] is configured to monitor the electric distribution system within a building to provide an additional signal about human activity. For example, when a user activates an electrical device, such as vacuum cleaner, within a household, the electrical device introduce electromagnetic interference signal within the electric distribution system of the building. The base station 110 [of sensing system 100] comprise a set of stored electromagnetic interference signal fingerprints within its computer storage media 114. The base station 110 utilize processors 112 and the fingerprints and/or a neural network [as example of machine learning] to map the electromagnetic interference signal to human activity. Further, in some embodiments, the base station 110 maps the electromagnetic interference signal to a particular appliance or electrical device using the electromagnetic interference signal fingerprints and/or the neural network. Accordingly, the system 100 is able to more accurately infer the likelihood of human occupancy based on the electromagnetic interference signal).
Henze also teaches or suggests: “a wireless connection” in “determining a number of users at the location based on a number of user devices with a wireless connection at the location”
(Henze ¶ [0029] 1st sentence: Fig.1 illustrates schematic of system 100 for wireless home identification and sensing. Other examples at ¶ [0032], ¶ [0076] 1st sentence)
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Andrei’s teachings to have included Henze’s teachings or suggestions in order to have provided improve detection of household occupancy (Henze ¶ [0004]- ¶ [0006], in view of MPEP 2143 G) using a rigorous neural network algorithm (Henze ¶ [0035] in view of MPEP 2143 G). The predictability of such modification would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Andrei ¶ [0158]-¶ [0161] in view of Henze ¶ [0085] - ¶ [0086].
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with energy consumption and household occupancy. In such combination each element merely would have performed the same analytical, benchmarking, econometric and managerial function as separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Andrei in view of Henze, the to be combined elements would have fitted together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the combination results would have been predictable (MPEP 2143 A).
* Further still *
Andrei / Henze does not explicitly recite:
- “smart consumption devices and a plurality of non-smart consumption devices” as claimed.
* However *
Ambati in analogous power consumption communication information teaches /suggests
- “smart consumption devices and a plurality of non-smart consumption devices”
(Ambati ¶ [0025] 2nd-3rd sentences: If a customer has smart appliances for some devices, the invention can find the share of smart devices in his/her total consumption. If the remaining consumption (due to non-smart devices) is above a threshold, the presence of power-consuming devices such as refrigerator or air conditioner (AC) that are not smart appliances can be inferred)
It would have been obvious to one of ordinary skilled in the art, before the effective filling date of the claimed invention, to have further modified Andrei / Henze’s “smart meter” to have further included explicitly recitation for “smart” and “non-smart consumption devices” in view of Ambati’s teachings in order to have provided better recommendations for smart appliances for these commonly used utilities and the corresponding potential savings achievable by adopting those appliances (Ambati ¶ [0025] 4th sentence in view of MPEP 2143 G and/or F). The predictability of such modification would have been corroborated by the broad knowledge of one of ordinary skills in the art as articulated by Andrei ¶ [0158]-¶ [0161] in view of Henze ¶ [0085] - ¶ [0086] and in further view of Ambati ¶ [0037], ¶ [0054], ¶ [0056], ¶ [0058].
Additionally, or alternatively, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with the gathering and analysis of power consumption. In such combination each element merely would have performed the same consumption data gathering and analysis function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Andrei / Henze in further view of Ambati, the to be combined elements would have fitted together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Claim 19. Andrei/Henze/Ambati teaches all limitations in claim 17 above. Further,
Andrei teaches/suggests: “commodity is one from a group consisting of: electricity, gas” (Andrei ¶ [0006] 3rd-4th sentences: consumption values and/or predicted energy consumption value may specify a total energy consumption value, optionally for a given fuel type used by an energy consumer (gas/electricity) and/or for a given time period. Any suitable measure may be used to represent energy/fuel consumption i.e kWh, m3 for gas), “water, and network bandwidth”
Claim 20 Andrei/Henze/Ambati teaches all the limitations in claim 17 above. Furthermore,
Andrei teaches or suggests “wherein the combination further includes other data relevant to the consumption of the commodity selected from a group consisting of”
- “information regarding weather forecasts”;
- “information received from user calendars”;
- “a current day of a week”; (Andrei ¶ [0146] 2nd sentence: data for weekdays (Monday-Friday) may be combined and clustered to produce a clustering indicative of weekday behaviour patterns, and data for weekend days combined to produce a clustering indicative of weekend behaviour patterns)
- “a current date” (Andrei ¶ [0148] 2nd sentence: determine behaviour patterns over a longer period, for example to generate a single clustering for July 31st, 2015);
- “a current time”; (Andrei ¶ [0135] 1st sentence: the time series data consists of half-hourly energy consumption values over a given time period this could be a day, a week, a month or any other time period).
- “data from a solar power generation system located behind the metering device”;
- “data from a smart thermostat”; “and”
- “data regarding active and inactive circuits in a smart breaker panel”.
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Claims 18 is rejected under 35 U.S.C. 103 as being unpatentable over:
Andrei / Henze / Ambati as applied to claim 17 above, and in further view of
Krishnaswamy et al, US 20240411353 A1 hereinafter Krishnaswamy. As per,
Claim 18 Andrei / Henze / Ambati teaches all limitations in claim 17 above. Furthermore,
Andrei / Henze / Ambati does not teach or suggest: “wherein the wireless connection is selected from a group consisting of a Wi-Fi connection or a Bluetooth connection” as claimed.
Krishnaswamy in analogous smart energy management teaches/suggests: “the wireless connection is selected from a group consisting of a Wi-Fi connection or a Bluetooth connection” (Krishnaswamy ¶ [0080] last 2 sentences: 1st computing device 104 may be aggregate node for example smart meter that aggregates energy info across different consuming devices such as the entities (102) in a home or an enterprise. In particular, 1st computing device (104) is implemented as smart meter. ¶ [0081] 1st-2nd sentences: In an embodiment, the one or more sensors may send the collected information to a local aggregate node such as a first computing device (104), which may forward the information to the system (110) for further analysis. In an example embodiment, the one or more sensors may communicate with the local aggregate node over suitable protocols including, but not limited to, Wireless Fidelity (Wi-Fi), Bluetooth, Zigbee, cellular 4G/5G/6G device to device communications, and the like).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Andrei/Henze/Ambati's "smart meter" to have further included Krishnaswamy’s teachings or suggestions to have further allowed the users to be equipped with the necessary information regarding their energy usage, to have empowered them to act effectively on the basis of that information for reviewing and acting upon the available information in an effective manner (Krishnaswamy ¶ [00065]- ¶ [0007], ¶ [0055] in view of MPEP 2143 G and/or F) while, at the same time, further optimizing the delivery of energy in the most efficient way (Krishnaswamy ¶ [0089] 4th sentence in view of MPEP 2143 G and/or F). The predictability of such modification would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Andrei ¶ [0158]-¶ [0161] in view of Henze ¶ [0085]-¶ [0086] in view of Ambati ¶ [0037], ¶ [0054], ¶ [0056], ¶ [0058], and in further view in view of Krishnaswamy ¶ [0120].
Additionally, or alternatively, the claimed invention could have also been viewed as a mere combination of old elements in a similar smart energy management field of endeavor. In such combination each element merely would have performed same analytical and data processing function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Andrei / Henze / Ambati in further view of Krishnaswamy, the to be combined elements would have fitted together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Conclusion
Following art is made of record and considered pertinent to Applicant’s disclosure:
- Oprea et al, Machine learning algorithms for short-term load forecast in residential buildings using smart meters, sensors and big data solutions. IEEE Access, 7,p177874-177p89, Dec 9, 2019
- CN 117455547 A teaching at its translated Abstract AI intelligent meter reading method comprises the following steps: firstly collecting the gas consumption data, creating and training an ARIMA prediction model and a random forest regression model to generate a first gas consumption prediction value and a second gas consumption prediction value; then, calculating the residual value of each gas consumption data point, namely the deviation between the first predicted value and the second predicted value, and taking twice of the standard deviation of the residual value as the threshold value, identifying the original gas consumption data point exceeding the threshold value as the abnormal value. performing the abnormal detection to the gas consumption data of each user, after the detected abnormal value user removes the abnormal data point, generating the gas charge according to the step charging mode. The method uses the random forest regression model and the residual value threshold value identification system to effectively improve the accuracy of the gas amount data processing so as to ensure the accuracy of the charging.
- US 20100324962 A1 ¶ [0074] 2nd-4th sentences: smart building manager 106 may communicably or functionally connect the smart grid (e.g., energy supply company, utility, ISO, broker, etc.) network to the metering and energy management devices in a building (e.g., devices built into appliances such as dishwashers or other smart appliances). In other words, the smart building manager 106 may be configured to route messages to and from other data-aware (e.g., Real Time Pricing (RTP) aware, curtailment signal aware, pricing aware, etc.) devices and the energy supply company. In this configuration, building subsystems that are not RTP aware will be managed by the DR layer 112 while devices that are RTP aware can get signals directly from the utility).
- US 20150276253 A1 ¶ [0096] 1st-2nd sentences: monitor 200 is a device that may be connected to the appliances 170… Specifically the monitor 200 may generate or collect data representative of environmental information available at the sensors… and how much electrical power the appliance is consuming in real time, which may be provided as energy consumption information from a conventional [interpreted as non-smart] or "smart" energy meter included as part of the appliance.
- US 20140277795 A1 ¶ [0086] The smart home environment 200 also can include a variety of non-communicating legacy appliances 240, such as old conventional washer/dryers, refrigerators, and the like which can be controlled, albeit coarsely (ON/OFF), by virtue of the wall plug interfaces 210. The smart home can further include a variety of partially communicating legacy appliances 242, such as IR-controlled wall air conditioners or other IR-controlled devices, which can be controlled by IR signals provided by the hazard detection units 204 or the light switches 208 or, in some embodiments, by using socket-based communication protocol such as powerline to communicate via a wall plug interface 210.
- US 20220414446 A1 teaches Trained models for discovering target device presence, reciting at ¶ [0015] Embodiments generate machine learning predictions to discover target device energy usage. Non-intrusive load monitoring and/or disaggregation refers to taking as input total energy usage at a source location (e.g., energy usage at a household provided by advanced metering infrastructure) and estimating energy usage for one or more appliances, electric vehicles, and other devices that use energy at the source location. Embodiments leverage a trained machine learning model that generates predictions about the presence or target device energy usage within general energy usage at a source location. For example, the target device may be a large appliance or electrical vehicle, the source location may be a household, and the trained machine learning model is configured to receive the energy usage of the household as input and predict whether the household energy usage includes target device energy usage. ¶ [0116] Embodiments generate machine learning predictions to discover target device energy usage. Non-intrusive load monitoring (“NILM”) and/or disaggregation refers to taking as input total energy usage at a source location (e.g., energy usage at a household provided by advanced metering infrastructure) and estimating energy usage for one or more appliances, electric vehicles, and other devices that use energy at the source location. Embodiments leverage a trained machine learning model that generates predictions about the presence or target device energy usage within general energy usage at a source location. For example, the target device may be a large appliance or electrical vehicle, the source location may be a household, and the trained machine learning model is configured to receive the energy usage of the household as input and predict whether the household energy usage includes target device energy usage.
- US 20230273581 A1 teaching Power control system and program
- US 20210182980 A1 teaching Modelling and forecasting electricity demand using ensemble learning
- US 20210125197 A1 teaching Managing utility rates and device optimization, and reciting at ¶ [0032] In some embodiments, the energy cost management system 221 may be configured to train one or more predictive models for analyzing input data (e.g., data from a plurality of energy consumption sources such as meters/sensors, data transmitted from the user device 201-1, 201-2, the third-party system 230, and/or data sources 211), to generate energy profile for an electric load/appliance and a property, or to perform rate analysis or electric tariff optimization. ¶ [0073] The machine learning and predictions module may process at least a portion of the input data 310 and output an energy profile of a property. The input data processed by the property forecasting module may include, for example, historical energy data such as smart meter readings or billing information from the utilities, property related data such as property square footage, property zoning and/or usage (e.g., residential, industrial, etc), property location (e.g., regional location, neighborhood, etc), or appliances of the property. The input datasets may optionally include information from grid and operational systems, such as sensor, SCADA, MDM, CIS, and other types of data identified herein. The input data may comprise data useful for forecasting the energy consumption of the property such as climate data (e.g., weather data). The input datasets may comprise data on one or more appliances having similar properties (e.g., property location) such that energy consumption of one or more new appliances may be forecasted without requesting the historical data of the same appliances.
- US 20150186827 A1 teaching Data-driven targeting of energy programs using time-series data and reciting at ¶ [0022] 3rd sentence: smart meter sensors 104 generate time-sequence consumption data 106 that is then centrally processed to learn about the customers, which includes segmenting the customers and forecasting consumption 108, the results of which are provided to be used in targeting customers for programs 112.
- US 20230419106 A1 teaching Machine Learning Models Trained to Generate Household Predictions Using Energy Data
- US 20190147465 A1 teaching demand prediction system and demand prediction method
- US 20140077968 A1 teaching Prediction of an estimated remaining utility usage via meter
- US 20220344934 A1 teaching Energy demand forecasting and sustainable energy management using machine learning
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). 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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/OCTAVIAN ROTARU/
Primary Examiner, Art Unit 3624 A
August 23rd, 2026
1 OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1364, 115 U.S.P.Q.2d 1090, 1092 (Fed Cir. 2015) (a new method of price optimization was found to be a fundamental economic concept)
2 Bilski v. Kappos, 561 U.S. 593, 611, 95 USPQ2d 1001, 1010 (2010) (claims to the concept of hedging are a "fundamental economic practice long prevalent in our system of commerce and taught in any introductory finance class.") Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1313, 120 USPQ2d 1353, 1356 (Fed. Cir. 2016) ("The category of abstract ideas embraces ‘fundamental economic practice[s] long prevalent in our system of commerce,’ … including ‘longstanding commercial practice[s]’")
3 Mayo, 566 U.S. at 79-80, 86-87, 101 USPQ2d at 1968-69
4 MPEP 2106.04(a): “examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible”.
5 MPEP 2106.04(a): “examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible”.
6 BSG Tech. LLC v. Buyseasons, Inc., 899 F.3d 1281, 1286, 127 USPQ2d 1688, 1691 (Fed. Cir. 2018);
7 MPEP 803.02 III A ¶4 citing Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009)(the entire element is disclosed by the prior art if one alternative in the Markush group is in the prior art).
8 MPEP 803.02 III A ¶4 citing Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009)(the entire element is disclosed by the prior art if one alternative in the Markush group is in the prior art).
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