DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is a non-final, first office action in response to the claims filed 03/11/2025.
Claims 1 – 11 are currently pending and have been examined.
Novel/Nonobvious Subject Matter
Claims 1 – 11 are not rejected over the prior art. The closest prior art of record fails to disclose or render obvious the instant claimed invention.
Bain et al. (US 20190372345 A1); (See, e.g., abs. [0007], & [0497]) teaches a system and method for estimating future prices for energy from multiple energy sources for a future time interval and presenting an updated GUI to an energy customer based on a time interval-specific calculation of the estimated prices.
Lee et al. (US 20150310461 A1); (See, e.g., [0032] & [0078]) teaches a system and method for collecting energy rate information and power usage information over a predetermined time period, predicting an amount of power consumption over a future time period, and determining an optimal usage schedule based on minimizing the amount of power consumption of the electronic device over the future time period based on the predicted amount of power consumption.
Vega et al. (US 20210123771 A1); (See, e.g., Fig. 33, [0287] & [0323] – [0329]) teaches a system and method for energy time series monitoring of actual versus forecasted energy usage based on key energy indicators such as costs and environmental footprint during a given period via an energy dashboard which is updated using new energy consumption and locational weather data retrieved and integrated at different time periods.
The cited references, however, fail to teach or render obvious the particular combination of claimed elements, including obtaining energy data, grid data, and price data for energy providers, generating ML inputs based on the energy data, grid data, and price data for each provider, forecasting generation source values for a first time interval based on a ML input based on energy data for each provider by a first machine learning model, forecasting grid condition values for the first time interval based on ML input based on grid data by a second machine learning model, forecasting price values for the first time interval based on provider price data ML input by a second machine learning model, forecasting an energy forecast based on the forecasted generation source values, grid condition values, and price values, and displaying an indication of timing for usage of energy provided by the energy provider within the first time interval.
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 – 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Claims 1 – 11 are directed to a method (i.e., a process). Therefore, claims 1 – 11 all fall within the one of the four statutory categories of invention.
Step 2A, Prong One
Independent claim 1 recites: “accessing… generation source data, grid information data, and price data associated with an energy provider; generating… forecasted generation source values… wherein the forecasted generation source values are generated for a first time interval; generating… forecasted grid condition values… wherein the forecasted grid condition values are generated for the first time interval; generating… forecasted price values… wherein the forecasted price values are generated for the first time interval; determining… an energy forecast for the first time interval, the energy forecast based at least in part on the forecasted generation source values, the forecasted grid condition values, and the forecasted price values; and causing… the energy forecast to be presented… the energy forecast providing an indication of timing for usage of energy provided by the energy provider, the indication of the timing of the usage of the energy being within the first time interval.”
The limitations stated above are processes that, under the broadest reasonable interpretation, covers performance of the limitation in a commercial interaction or while managing personal behavior or relationships or interactions between people. That is, the functions in the context of the claims encompass providing an energy customer energy optimization suggestions. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in a commercial interaction, or while managing personal behavior or relationships or interactions between people, then it falls within the "Certain Methods of Organizing Human Activity" grouping of abstract ideas e.g., “commercial or legal interactions (including marketing or sales activities or behaviors; business relations)” as well as “managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).” Accordingly, the claims recite an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. Claim 1, as a whole, amounts to merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent), as well as generally linking the recited judicial exception to a particular field or technological environment.
Claim 1 recites the additional generic computer elements of “by a computing system,” “by the computing system and via a first machine learning model,” “by the computing system and via a second machine learning model,” “by the computing system and via a third machine learning model,” and “on a user device.” Claim 1 also recite the additional elements of: “generating, by the computing system and using machine learning techniques, a first machine learning input based at least in part on the generation source data, a second machine learning input based at least in part on the grid information data, and a third machine learning input based at least in part on the price data,” “based at least in part on the first machine learning input,” “based at least in part on the second machine learning input,” and “based at least in part on the third machine learning input.”
The additional elements of “by a computing system,” “by the computing system and via a first machine learning model,” “by the computing system and via a second machine learning model,” “by the computing system and via a third machine learning model,” and “on a user device” are recited at a high-level of generality, such that, when viewed as whole/ordered combination, it amounts to no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)).
The additional elements of “generating, by the computing system and using machine learning techniques, a first machine learning input based at least in part on the generation source data, a second machine learning input based at least in part on the grid information data, and a third machine learning input based at least in part on the price data,” “based at least in part on the first machine learning input,” “based at least in part on the second machine learning input,” and “based at least in part on the third machine learning input” are recited at a high-level of, and when viewed as whole/ordered combination, amounts to no more than a mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)), as well as merely generally linking the recited judicial exception to a particular technological environment or field of use of machine learning (See MPEP 2106.05(I)(A) & MPEP 2106.05(h)).
Accordingly, these additional elements, when viewed as a whole/ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Thus, the claim is directed to an abstract idea.
Step 2B
As discussed above with respect to Step 2A Prong Two, the additional elements amount to no more than merely invoking generic components as a tool to perform the abstract idea or “apply it” (or an equivalent), as well as generally linking the recited judicial exception to a particular field or technological environment, and do not provide integration of the recited abstract ideas into a practical application. The same analysis applies here in Step 2B, i.e., merely invoking the generic components as a tool to perform the abstract idea or “apply it” (See MPEP 2106.05(f)), as well as generally linking the recited judicial exception to a particular technological environment or field of use (See MPEP 2106.05(I)(A) & MPEP 2106.05(h)), does not integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B.
Therefore, the additional generic computer elements of “by a computing system,” “by the computing system and via a first machine learning model,” “by the computing system and via a second machine learning model,” “by the computing system and via a third machine learning model,” and “on a user device,” as well as the additional elements of “generating, by the computing system and using machine learning techniques, a first machine learning input based at least in part on the generation source data, a second machine learning input based at least in part on the grid information data, and a third machine learning input based at least in part on the price data,” “based at least in part on the first machine learning input,” “based at least in part on the second machine learning input,” and “based at least in part on the third machine learning input,” fail to integrate the abstract idea into a practical application at Step 2A or provide an inventive concept at Step 2B. Thus, even when viewed as a whole/ordered combination, nothing in the claims adds significantly more (i.e., an inventive concept) to the abstract idea. There is no indication that the combination of elements, taken both individually and as an ordered combination, improves the functioning of a computer or improves any other technology. Thus, the claims are not patent eligible.
Furthermore, dependent claims 2 – 11 are merely directed to the particulars of the abstract idea and likewise do not add significantly more to the above-identified judicial exception. The additional element of “user interface elements” in claim 2 amounts no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)). The additional element of “on a mobile device” in claim 3 amounts no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)). The additional element of “in a home automation application” in claim 5 amounts no more than mere instruction to apply the judicial exception using generic computer components or “apply it” (See MPEP 2106.05(f)). The limitations of the claims, when considered both individually and as an ordered combination, do not transform the abstract idea that they recite into patent-eligible subject matter because the claims simply instruct the practitioner to implement the abstract idea with generic computer components that conduct generic computer functions within a certain field of use, and thus are ineligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure: Bain et al. (US 20190372345 A1); Lee et al. (US 20150310461 A1); Vega et al. (US 20210123771 A1).
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/BRYAN J KIRK/Examiner, Art Unit 3628