DETAILED ACTION
This non-final office action is in response to communications fined on 1/19/26.
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 .
Election/Restrictions
Applicant’s election without traverse of claims 1-9 in the reply filed on 1/19/26 is acknowledged.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7, 9, 21-27 and 29-31 are rejected under 35 U.S.C. 103 as being unpatentable over Matsuoka et al., US Patent Application Publication no. 2014/0277769 [Matsuoka], in view of Saxena et al., US Patent Application Publication no. 2022/0294221 [Saxena].
Regarding claims 1, 21 and 30, Matsuoka discloses a system comprising:
one or more computing devices [electronic devices, paragraph 0203];
one or more non-transitory storage media storing instructions which, when executed by one or more computing devices, cause:
predicting a load capacity to be made available for an upcoming DR event based, at least in part on current DR event data [an estimate of the aggregate DR energy shifting likely to result from a subset of energy consumers qualified for participation in a particular DR event is determined, paragraph 0199];
determining, based, at least in part, on the predicted load capacity made available for an upcoming DR event, that there is not sufficient load capacity to balance energy supply and demand during the upcoming DR event [the estimated aggregate DR energy shifting is compared to the desired DR energy shifting and it is determined whether the estimated aggregate DR energy shifting is less than then the desired DR energy shifting, paragraphs 0200-0201]; and
responsive to determining that the load capacity is not sufficient to balance the energy supply and demand during the upcoming DR event, automatically performing one or more load capacity increasing actions [if the estimated aggregate DR energy shifting is less than the desired DR energy shifting, then the size of the subset of energy consumers qualified for participation in a particular DR event is increased, paragraph 0201].
Matsuoka, as described above, discloses determining a subset of energy consumers qualified for participation in a particular DR event and predicting an estimated aggregate DR energy shifting based on the subset of energy consumers qualified for participation in a particular DR event. Matsuoka further discloses that the subset of energy qualified for participation in a particular DR event is determined based on historical DR event data, such as prior participation levels in past DR events. Matsuoka does not disclose that the estimated aggregate DR energy shifting based on the subset of energy consumers qualified for participation in a particular DR event is determined using a machine learning model that is trained based on a training data set that includes historical DR event data. Like Matsuoka, Saxena discloses a system that predicts user DR responses based on historical DR response data. Specifically, Saxena discloses using a DR performance optimization engine that builds and trains a DR user pooling machine learning model based on DR historical data and historical weather data [paragraphs 0022-0023, 0028 and 0030]. Since using machine learning to model DR even participation in DR systems was known in the art before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to apply the Saxena teachings to the Matsuoka DR system by incorporating machine learning to the estimate aggregate DR energy shifting for a particular DR event improve responsivity to DR programs by DR users [Saxena, paragraph 0076].
Regarding claims 2, 22 and 31, Saxena further discloses that the historical DR event data comprises historical load-shedding participation data for historical DR events [records of demands sent in the past and responses thereto by respective DR participants, paragraph 0023].
Regarding claims 3 and 23, Saxena further discloses that the historical DR event data comprises historical incentive compensation data [reward rates per responsivity, paragraph 0023].
Regarding claims 4 and 24, Saxena further discloses that weather data comprises one or more of: extreme weather probability projections, weather forecast data, or detected weather conditions [weather and temperature conditions, paragraph 0030].
Regarding claims 5 and 25, Matsuoka further discloses that the current DR event data comprises one or more of: real-time load-shedding participation data for an upcoming DR event or current pricing data for incentive compensation [an estimate of the aggregate DR energy shifting likely to result from a subset of energy consumers currently qualified for participation in a particular DR event is determined, paragraph 0199].
Regarding claims 6 and 26, Matsuoka further discloses that a load capacity increasing action comprises one of: increasing incentive compensation offered for the upcoming DR event, increasing a level of participation of a set of dynamically-enrolled users, or causing a request for additional participation in increasing load capacity to be sent to one or more users [if the estimated aggregate DR energy shifting is less than the desired DR energy shifting, then the size of the subset of energy consumers qualified for participation in a particular DR event is increased, paragraph 0201].
Regarding claims 7 and 27, Saxena further discloses receiving a plurality of responses from the plurality of customers, wherein each response in a subset of the plurality of responses indicates approval in participating in increasing load capacity for the upcoming DR event [DR agreements are structured in a way that when the supply grid sends out demands to reduce power consumption, the customers respond to the demands by promptly cutting off the energy consumption that is specified in the demands, paragraph 0016].
Regarding claims 9 and 29, Matsuoka further discloses determining a first load capacity to be made available for the upcoming DR event based on a set of customers that have agreed to participate in the upcoming DR event; aggregating the first load capacity with the predicted load capacity to generate an aggregated load capacity to be made available for the upcoming DR event; wherein determining that there is not sufficient load shed is also based on the aggregated load capacity [the estimated aggregate DR energy shifting is compared to the desired DR energy shifting and it is determined whether the estimated aggregate DR energy shifting is less than then the desired DR energy shifting, paragraphs 0200-0201].
Claims 8 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Matsuoka et al., US Patent Application Publication no. 2014/0277769 [Matsuoka] and Saxena et al., US Patent Application Publication no. 2022/0294221 [Saxena], in view of Applicant’s Admitted Prior Art [AAPA].
Regarding claims 8 and 28, Matsuoka and Saxena, as described above, discloses that the DR system sends demands and receives responses for customers to perform load shedding. Matsuoka and Saxena do not disclose that the DR system sends demands and receives responses for customers to add load supply. Like Matsuoka and Saxena, AAPA discloses an electric grid DR system that sends demands to customers during peak demand periods. AAPA further discloses that the demands may include both load shedding demands and load adding demands [paragraph 0005]. Since electric grid DR systems that include both peak demand load shedding and peak demand load adding peak capabilities were known in the art before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to apply the Matsuoka and Saxena teachings to known electric grid DR systems that include both peak demand load shedding and peak demand load adding peak capabilities in order to improve responsivity to DR programs by DR users [Saxena, paragraph 0076].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Black et al., US Patent Application Publication no. 2012/0136496 discloses estimating demand response for utility DR programs.
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/PAUL B YANCHUS III/Primary Examiner, Art Unit 2115 February 21, 2026