Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 1-8, 10-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) (1, 13, & 19) recite(s) data processing steps of receiving data indicative of power consumption at a building, generating a plurality of features from this data, and executing a model trained by machine learning to detect that the electrical vehicle charges at the building which are considered mental processes (observation, evaluation, and judgement). This judicial exception is not integrated into a practical application because generically recited computer elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because storing and retrieving information in memory are well-understood, routine, conventional computer functions as recognized by the court decisions listed in MPEP § 2106.05(d).
Claims 2-8, 10-12, 14-18, & 20 do not appear to overcome this deficiency.
Claim 9 appears to integrate the abstract idea into a practical application because it causes a utility to generate power based on a transmitted message.
Claims 19 & 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim(s) is/are directed to a signal per se. The claims should instead recite a “non-transitory computer storage media”.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3, 7, 12, 13, 18, & 19 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Huang et al. (USPGPN 2024/0175906 – filed Jul. 2023).
Regarding Claim 1, Huang (Fig.2) teaches a system, comprising:
a data processing system (201) comprising one or more memory devices coupled with one or more processors (¶0152: computer system 700 includes processor 710 and memory 715 and can be used implement data processing system 150 or metering system 201), the data processing system to:
receive data indicating power consumption at a building at a plurality of points in time (¶0059: data collector 204 obtains electrical waveform data; Fig.4B shows that waveform data is at a plurality of points in time), the power consumption at the building including power consumption of an electric vehicle charging at the building (Fig.4B: Electric Vehicle);
generate a plurality of features from the power consumption at the building at the plurality of points in time (206; ¶0068: power metric over a time window); and
execute a model trained by machine learning based on the plurality of features to detect that the electric vehicle charges at the building (¶0090: metrics input into the model trained with machine learning).
Regarding Claim 3, Huang further teaches receive timeseries data indicating power consumption levels at the building at the plurality of points in time (¶0059: data collector 204 obtains electrical waveform data); and
execute timeseries processing to generate the plurality of features from the timeseries data (¶0068: metric generator 206 determines power metric over time interval).
Regarding Claim 7, Huang further teaches the model is not trained based on a charging profile of the electric vehicle (Fig.3: 304 offline training stage for training the model does not use charging profiles of various types of electric vehicles and instead uses medium-resolution load data of a building, 302, with metrics from the high-resolution current data, ¶0127).
Regarding Claim 12, Huang further teaches generate the plurality of features from only the power consumption at the building at the plurality of points in time measured by a meter disposed at the building; and execute the model trained by machine learning based on only the plurality of features (Fig.3, 306: only real-time detection parameters are generated and passed to the model for executing).
Regarding Claims 13 & 19, Huang teaches a method (¶0019: stored on a non-transitory computer-readable medium for cause a processor to execute instructions to perform the method), comprising:
receiving, by one or more processing circuits (¶0152: computer system 700 includes processor 710 and memory 715 and can be used implement data processing system 150 or metering system 201), data indicating power consumption at a building at a plurality of points in time, the power consumption at the building (¶0059: data collector 204 obtains electrical waveform data; Fig.4B shows that waveform data is at a plurality of points in time), including power consumption of an electric vehicle charging at the building (Fig.4B: Electric Vehicle);
generating, by the one or more processing circuits, a plurality of features from the power consumption at the building at the plurality of points in time (206; ¶0068: power metric over a time window); and
executing, by the one or more processing circuits, a model trained by machine learning based on the plurality of features to detect that the electric vehicle charges at the building (¶0090: metrics input into the model trained with machine learning).
Regarding Claim 18, Huang further teaches generating, by the one or more processing circuits, the plurality of features from only the power consumption at the building at the plurality of points in time measured by a meter disposed at the building; and executing, by the one or more processing circuits, the model trained by machine learning based on only the plurality of features (Fig.3, 306: only real-time detection parameters are generated and passed to the model for executing).
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.
Claim(s) 2, 9, & 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang.
Regarding Claim 2, Huang discloses the claimed invention except for the data indicating power consumption levels at the building at the plurality of points in time is not disclosed as at least one year of data and is only disclosed as historical. It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to use a plurality of points in time for at least one year, since it has been held that discovering an optimum value of a result effective variable involves only routine skill in the art. In re Boesch, 617 F.2d 272, 205 USPQ 215 (CCPA 1980). Doing so allows the system or a user of the system a larger time window of EV charging detection to improve the user experience and allow for recognition of charging patterns.
Regarding Claim 9, Huang teaches generate a message comprising and an indication that the electric vehicle charges at the building (¶0151: provide an alert that an EV charging occurrence is detected); and
transmit the message to a computing system of a utility to cause the utility to generate power for a power grid based on the message (¶0151: grid may generate or allocate electricity based on a received EV charging event, transmitted through the data processing system).
Huang teaches the claimed invention except it does not explicitly state providing an address of the building. It would have been obvious to one having ordinary skill in the art effective filing date of the invention to include an address of the building since it was known in the art that building addresses are used to identify homes for electrical service and so providing the address would allow the utility to properly provide additionally generated electricity to the home with an occurring EV charging event.
Regarding Claim 14, Huang discloses the claimed invention except for the data indicating power consumption levels at the building at the plurality of points in time is not disclosed as at least one year of data and is only disclosed as historical. It would have been obvious to one having ordinary skill in the art at the effective filing date of the invention to use a plurality of points in time for at least one year, since it has been held that discovering an optimum value of a result effective variable involves only routine skill in the art. In re Boesch, 617 F.2d 272, 205 USPQ 215 (CCPA 1980). Doing so allows the system or a user of the system a larger time window of EV charging detection to improve the user experience and allow for recognition of charging patterns.
Claim(s) 4 & 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang, in view of Aggarwal et al. (USPGPN 2019/0154741).
Regarding Claim 4, Huang further teaches execute timeseries processing based on a parameter to generate the plurality of features from the data (as disclosed in the rejection of claim 3 above).
Huang fails to explicitly teach generate, based on a classification of the model that detects that the electric vehicle charges at the building, an update to the parameter; and
execute the timeseries processing based on the update to the parameter to generate a plurality of second features from the data.
However, Aggarwal teaches a detection system for electric vehicle charging which includes steps of generating, based on a classification of a model that detects that the electric vehicle charges at a building (¶0036: prior detections), an update to a parameter (¶0036: parameters may be adjusted) used to generate a plurality of features from energy data (¶0032: candidate search step, 110, looks for patterns associated with EV charging), and executing timeseries processing based on the update to the parameter to generate a plurality of second features from the data (Fig.1, steps 110->140 are repeated with updated parameters from 150).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system taught by Huang with Aggarwal to include generating, based on a classification of the model that detects that the electric vehicle charges at the building, an update to the parameter; and executing the timeseries processing based on the update to the parameter to generate a plurality of second features from the data. Doing so allows the system to be improved by correcting any incorrectly detected signals, as evidenced by Aggarwal (¶0036).
Regarding Claim 15, Huang further teaches executing, by the one or more processing circuits, timeseries processing based on a parameter to generate the plurality of features from the data (as disclosed in the rejection of claim 3 above).
Huang fails to explicitly teach generating, by the one or more processing circuits, based on a classification of the model that detects that the electric vehicle charges at the building, an update to the parameter; and
executing, by the one or more processing circuits, the timeseries processing based on the update to the parameter to generate a plurality of second features from the data.
However, Aggarwal teaches a detection system for electric vehicle charging which includes steps of generating, based on a classification of a model that detects that the electric vehicle charges at a building (¶0036: prior detections), an update to a parameter (¶0036: parameters may be adjusted) used to generate a plurality of features from energy data (¶0032: candidate search step, 110, looks for patterns associated with EV charging), and executing timeseries processing based on the update to the parameter to generate a plurality of second features from the data (Fig.1, steps 110->140 are repeated with updated parameters from 150).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system taught by Huang with Aggarwal to include generating, based on a classification of the model that detects that the electric vehicle charges at the building, an update to the parameter; and executing the timeseries processing based on the update to the parameter to generate a plurality of second features from the data. Doing so allows the system to be improved by correcting any incorrectly detected signals, as evidenced by Aggarwal (¶0036).
Claim(s) 5, 6, 8, 10, 16, 17, & 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang, in view of Decker (USPGPN 2024/01449736 – filed Nov. 2022).
Regarding Claim 5, Huang further teaches the plurality of features including:
indications of spikes in the power consumption at the building greater than a threshold (¶0076: power at or above a predetermined threshold).
Huang fails to explicitly teach the plurality of features including indications of a patterns of the spikes in the power consumption at the building.
However, Decker teaches a plurality of features including indications of a patterns of the spikes in the power consumption at the building (¶0037: temporal sequencing to identify features of the power consumption data that are likely to occur in series with particular events or at certain times).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system taught by Huang with Decker to include indications of a patterns of the spikes in the power consumption. Providing this information allows the machine-learning module to identify operations at a premises using a significant indicator, particular times of charging, as evidenced by Decker (¶0037).
Regarding Claim 6, Huang further teaches generate a plurality of first features including indications of spikes in the power consumption at the building greater than a threshold (¶0076: power at or above a predetermined threshold).
Huang fails to explicitly teach generate a plurality of second features including indications of a pattern of the spikes in the power consumption at the building;
merge the plurality of first features and the plurality of second features into one signal; and
execute the model trained by machine learning based on the one signal to detect that the electric vehicle charges at the building.
However, Decker teaches generate a plurality of second features including indications of a pattern of the spikes in the power consumption at the building (¶0037: temporal sequencing to identify features of the power consumption data that are likely to occur in series with particular events or at certain times);
merge the plurality of first features and the plurality of second features into one signal (Fig.3: 304/306, include consumption peaks and data anomalies (¶0035), which are also used with the pattern of the spikes in the power consumption in the data process to generate the classification 310 (¶0038)); and
execute the model trained by machine learning based on the one signal to detect that the electric vehicle charges at the building (Fig.3L 312).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system taught by Huang with Decker to include generate a plurality of second features including indications of a pattern of the spikes in the power consumption, merging the plurality of first features and second features, and executing the model based on the one signal to detect that the electric vehicles charges at the building. Providing this information allows the machine-learning module to identify operations at a premises using a significant indicator, particular times of charging, as evidenced by Decker (¶0037).
Regarding Claim 8, Huang fails to explicitly teach the data processing system to:
receive indications that electric vehicles charge at a first set of buildings and no electric vehicle charges at a second set of buildings; and
train the model with machine learning based on:
the indications that the electric vehicles charge at the first set of buildings and no electric vehicle charges at the second set of buildings; and
the plurality of features.
However, Decker (Fig.4) teaches receiving indications that electric vehicles charge at a first set of buildings and no electric vehicle charges at a second set of buildings (402; ¶0039: training and validation consumption data includes multiple premises taken over 52 weeks and can be in subsets of data with 7 days of consumption, labeled as not performing charging, or Level 1 or Level 2 charging); and
train the model with machine learning based on:
the indications that the electric vehicles charge at the first set of buildings and no electric vehicle charges at the second set of buildings (406); and
the plurality of features (Fig.3, 304/306; ¶0035: data anomalies or consumption peaks would be used to train the model).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system taught by Huang with Decker to include receiving indications that electric vehicles charge at a first set of buildings and no electric vehicle charges at a second set of buildings and training the model with machine learning based on the indications and plurality of features. Doing so allows the system to continually learn using evolving real-world consumption data and allows the system to differentiate between charging levels, as evidenced by Decker (¶0041 & 0043).
Regarding Claim 10, Huang fails to explicitly teach generate a charging profile of the electric vehicle at the building based on at least one feature of the plurality of features responsive to the detection that the electric vehicle charges at the building.
However, Decker teaches predicting future charging which includes charging of an EV and a type of charging operation (¶0026) using the trained model, indicating that for a given location, a charging profile has been generated so that the prediction can be made for future planned energy generation, based on a feature (Fig.3, 304/306).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system taught by Huang with Decker to include generating a charging profile of the electric vehicle at the building based on at least one feature of the plurality of features responsive to the detection that the electric vehicle charges at the building. Doing so allows the utility to plan for capacity changes over time within the power distribution network, as evidenced by Decker (¶0026).
Regarding Claim 16, Huang further teaches the plurality of features including:
indications of spikes in the power consumption at the building greater than a threshold (¶0076: power at or above a predetermined threshold).
Huang fails to explicitly teach the plurality of features including indications of a patterns of the spikes in the power consumption at the building.
However, Decker teaches a plurality of features including indications of a patterns of the spikes in the power consumption at the building (¶0037: temporal sequencing to identify features of the power consumption data that are likely to occur in series with particular events or at certain times).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system taught by Huang with Decker to include indications of a patterns of the spikes in the power consumption. Providing this information allows the machine-learning module to identify operations at a premises using a significant indicator, particular times of charging, as evidenced by Decker (¶0037).
Regarding Claims 17 & 20, Huang further teaches generating, by the one or more processing circuits, a plurality of first features including indications of spikes in the power consumption at the building greater than a threshold (¶0076: power at or above a predetermined threshold).
Huang fails to explicitly teach generating, by the one or more processing circuits, a plurality of second features including indications of a pattern of the spikes in the power consumption at the building;
merging, by the one or more processing circuits, the plurality of first features and the plurality of second features into one signal; and
executing, by the one or more processing circuits, the model trained by machine learning based on the one signal to detect that the electric vehicle charges at the building.
However, Decker teaches generating a plurality of second features including indications of a pattern of the spikes in the power consumption at the building (¶0037: temporal sequencing to identify features of the power consumption data that are likely to occur in series with particular events or at certain times);
merging the plurality of first features and the plurality of second features into one signal (Fig.3: 304/306, include consumption peaks and data anomalies (¶0035), which are also used with the pattern of the spikes in the power consumption in the data process to generate the classification 310 (¶0038)); and
executing the model trained by machine learning based on the one signal to detect that the electric vehicle charges at the building (Fig.3L 312).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system taught by Huang with Decker to include generate a plurality of second features including indications of a pattern of the spikes in the power consumption, merging the plurality of first features and second features, and executing the model based on the one signal to detect that the electric vehicles charges at the building. Providing this information allows the machine-learning module to identify operations at a premises using a significant indicator, particular times of charging, as evidenced by Decker (¶0037).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huang, in view of Wolfe et al. (USPGPN 2024/0222971 – effectively filed May 2021).
Regarding Claim 11, Huang teaches that the model used can include a convolutional neural network model, long-short-term-memory model, support vector machine, or other types of models (¶0083) executing based on the plurality of features to output a classification that indicates that the electric vehicle charges at the building.
Huang fails to explicitly teach execute a gradient boost model based on the plurality of features to output a classification that indicates that the electric vehicle charges at the building.
However, Wolfe teaches that a gradient boosting model can be used in place of a support vector machine model (¶0100: can comprise computer implemented methods including support vector machine model and/or gradient boosting).
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system taught by Huang with Wolfe to execute a gradient boost model based on the plurality of features to output a classification. Doing so allows for improved accuracy of the model, as is a known benefit in the art of gradient boosting.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
King et al. (USPGPN 2026/0091700) teaches a system for detecting charging of an EV.
Lu et al. (USPGPN 2020/0122598) teaches a system for detecting charging of an EV.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN P ONDRASIK whose telephone number is (703)756-1963. The examiner can normally be reached Monday - Friday 7:30 a.m. - 5 p.m. ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Julian Huffman can be reached at (571) 272-2147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN P ONDRASIK/Examiner, Art Unit 2859
/JULIAN D HUFFMAN/Supervisory Patent Examiner, Art Unit 2859
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