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 .
Response to Amendment
Examiner’s acknowledges applicant’s response regarding 112f interpretation.
Applicant’s amendments to claims 1,8 and 15 have overcome each and every rejection made under 35 U.S.C. 112(b) set forth in the previous office action mailed on 01/23/2026. Therefore the rejections made under 35 U.S.C.112(b) are withdrawn.
Applicant’s amendment to claims 1,8 and 15 have overcome each and every rejection made under 35 U.S.C. 101 set forth in the previous office action mailed on 01/23/2026. Therefore the rejections made under 35 U.S.C.101 are withdrawn.
Applicant’s argument regarding Yan et al. does not teach associating ambient temperature data with terms of the time series of consumption-change data because Yan et al. does not teach consumption change data at all and also does not teach specifically paring ambient temperature readings with individual terms in the consumption -change range time series have been fully considered but not found persuasive. Examiner’s response below.
Examiner relied upon primary reference Lu et al. for teaching consumption change data as taught in [0040],[0041],[0043] and [0062]. Than Examiner used Yan et al. to teach the concept of associating load data which is consumption change data in view of Lu et al. with temperature data for a period of time. Yan et al. was not used particularly to teach consumption change data but rather used to teach the concept that time series load data is associated with temperature data to identify various load signatures for a period of time. Also the claim does not really define how electrical consumption data is converted to consumption-change data other than reciting converting time-series electrical consumption data to time series consumption-change data. The difference between timeseries electrical consumption data and time series consumption-change data is not clear and not defined. Without proper description of each of the data types, Yan et al. does teach time series load data as time series energy consumption data/ energy change data to identify load signatures. Furthermore applicant argued Yan et al. does not teach pairing specific ambient temperature readings for a time series with corresponding terms of consumption change time series data. But the claim does not mention multiple ambient temperature readings but rather recites temperature data only over the time range. With BRI ambient temperature data over the time range could mean one temperature reading for the entire time range or change in temperature or temperature time series data for the time range. No definition of ambient temperature data is provided. Yan et al. explicitly teaches in [0023],[0053] and [0059] to associate ambient temperature data with load time series data for a period of time (time range). That is for a period of time, each load time series data corresponds to the ambient temperature (temperature obtained from weather data). Without lack of definitions and descriptions of ambient temperature data, time series consumption data and based on the reasons discussed above, Yan et al. does teach the limitation, associating temperature data with terms of the timeseries of consumption-change data to thereby create input data over the time range.
Applicant further argued Lu et al does not teach (a) associating ambient temperature data with consumption change data to create input data, (b) processing that input data through a machine learned model to detect EV charging, and then (c) responsive to that detection, directing changes to transformers, service site assignments, or phase balancing. Applicant did not mention any particular reason distinguishing how each of the above limitation differs or not taught by Lu et al. There is no definition of ambient temperature data, electrical-consumption data and consumption change data on the claim that distinguishes the electrical consumption data indicating change in consumption data for EV charging over the period of time as taught by Lu et al. Then inputting the pre-processed data as input to the machine learning model to identify/detect EV charging event. Based on the detection/identified EV charging event reroute power flow to a different transformer that is changing transformer or size up the transformer as taught by Lu et al. The claim mentioned to perform only one action not multiple actions. Also the claim recites to detect EV charging event from time series consumption change data created over the time range from collected consumption load data over the time range. The claimed time range does not specify real-time data collection period or future time period. Therefore the claimed detection of EV charging event is based of current time or previous time not the real-time or on going time period such that the transformer is changed while the EV charging event is going on as argued by the applicant. As such Lu et al. teaches responsive to detection of an EV charging event, directing at least one of: changing a transformer used by the service site. Both the references Lu et al. and Yan et al. are solving the same problem that is distinguishing between different power events based on analyzing electrical power consumption data. Both the references are trying to solve the same problem as the application that is trying to identify the increased load on the grid based on load consumption data. Some of ordinary skill in the art will look into the each of the reference to yield predictable results of distinguishing between various load consumption events including EV charging as well. There is no teaching away on Yan et al. why the load signatures cannot include EV charging events or cannot be identified by the machine learning model or technique rather applying the known technique of detecting EV charging event as taught by Lu to identify various load signatures as taught by Yan et al. to yield predictable results of distinguishing or detecting various consumption events as stated in MPEP.2143.I.(B). As such combination of Lu et al. and Yan et al. teach the claim 1 as a whole.
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) 1-4, 7,15-18 are rejected under 35 U.S.C. 103 as being unpatentable over Lu et al. (US 20200122598 A1) in view of Yan et al. (US 20210088563 A1).
For claim 1, Lu et al. teaches, a method of detecting electric vehicle (EV) charging (systems and methods for identifying electric charge events, [0014]),
comprising:
obtaining a time-series of electrical-consumption data of a service site
over a time-range (obtaining time series electricity consumption data from a premise or location (service site) for a period of time, [0024], [0026] and [0040]);
converting the time-series of electrical-consumption data into a timeseries of consumption-change data (by analyzing the electricity consumption data in conjunction to EV charging activity data over the period of time, consumption data related to EV charging event or consumption change can be determined for the obtained time series electricity consumption data, [0040], [0041], [0043],[0062] and see also [0044] reciting pre-processing raw KWh data particular magnitude and variance (consumption change) over a period of time);
providing the input data over the time-range to a machine-learned model (pre-processed data is fed into the machine learning model to determine EV charging event by analyzing the obtained and processed electricity consumption data, [0092] and [0093]);
processing the input data over the time-range in the machine-learned
model to generate output, wherein the output comprises a likelihood value of at
least one EV charging event during the time-range (the trained machine learning
model analyze the processed electricity consumption data based on certain conditions
of charging such as magnitude of the initial sharp jump in kWh when the suspected EV
charge starts, a magnitude of the drop in kWh when the suspected EV charge ends1, a
duration of the suspected EV charge event, a standard deviation of kWh values during
the suspected EV charge, a total kWh usage during the suspected EV charge, a
maximum and/or minimum kWh value during the suspected EV charge. After analysis
the trained machine learning model outputs a value indicating EV charging events, [0084] and [0092]-[0094], see also [0059]);
receiving output from the machine-learned model (After analysis the trained
machine learning model outputs a value indicating EV charging events, [0084] and
[0092]-[0094], see also [0059]); and
responsive to detection of an EV charging event performing at least one of: changing a transformer used by the service site (based on identified EV
charging events, corrective actions can be taken by rerouting power flow from a
different transformer, replacing or resizing the transformer for higher capacity, [0016]
and [0099]);
changing a number of service sites served by the transformer (based on
identified EV charging events, corrective actions can be taken by rerouting power flow
from a different transformer, replacing or resizing the transformer for higher capacity,
[0016] and [0099]); or
changing a phase of electricity provided to the transformer.
Lu et al. does not teach the details of associating temperature data with terms of
the time-series of consumption-change data to thereby create input data over the time- range. However Lu et al. explicitly teaches to process the obtained raw electricity
consumption data to obtain consumption change data as taught in [0043], [0062], [0084] and [0092]-[0094], see also [0059].
Yan et a. teaches, associating temperature data with terms of the timeseries
of consumption-change data2 to thereby create input data over the time-range
(time series data or power measurements from PMU are obtained followed by
FTT transformation for consumption change data and weather data where the PMU is
located is also obtained for a particular time window. That is associating temperature
data (ambient temperature from weather data) with consumption change data or time series power measurement/consumption data. The time series power measurement data indicates consumption data which can be analyzed to detect change in consumption/consumption change data to identify various signatures indicating power events, [0023],[0053], [0059], see also [0049]).
Lu et al. and Yan et al. are analogous art because that are from similar problem
solving area of distinguishing between different power events based on analyzing
time stamped electrical power consumption data.
Therefore it would have been obvious before the effective filing date of the
claimed invention to a person of ordinary skill in the art to modify the system detecting
EV charging event based on analyzing electrical consumption data and consumption
change data by applying the known technique of associating ambient temperature data
to consumption change data as taught by Yan et al. to yield predictable results for
distinguishing between EV charging events and other events such as weather related
HVAC usage as mentioned by Lu et al. in [0003].
Regarding claim 2 combination of Lu et al. and Yan et al. teach the method of
claim 1. In addition Lu et al. teaches, wherein processing the input data over the
time-range in the machine-learned model (machine learning model reading collected timeseries kWh data, [0092]) comprises:
recognizing a change in consumption between two terms in the timeseries
of consumption-change data, wherein the change in consumption is greater
than a threshold value (the trained machine learning model can identify the start and
stop time for EV charging event (change in consumption between two terms) by
analyzing the collected kWh data and comparing it with known EV charge event
features (such as kWh level remains at that high level for a sustained period of time and
then drops thus the subsequent term in timeseries data will be much less than previous
kWh data). Then the identified/suspected EV charge event is compared to a set threshold as a confirmation for EV charge event, [0063],[0064] and [0093]).
Regarding claim 3 combination of Lu et al. and Yan et al. teach the method of
claim 1. In addition Yan et al. teaches, wherein the machine-learned model
comprises a convolutional neural network (CNN) (neural network model used by
machine learning model to differentiate between different power signatures, [0021 ],
[0053], [0054]), and wherein processing the input data over the time-range in the
CNN (machine learning technique or model uses real time power data to identify various
power signatures, [0021]) comprises.
In addition Lu et al. teaches, determining weights to associate with terms m
the time-series of consumption-change data (" ... Each kWh value in each time interval is encoded with one of the encoded letters3 based on the usage level as
discussed. A resulting encoding string 325 for the series of time intervals of the account
data 300 is shown below the graph of FIG. 3. Each encoded letter of the encoded string
325 is positioned in an approximate alignment below its corresponding kWh value and
time interval. .. ", [0048] and [0050]); and
using the weights associated with a plurality of terms to derive the output
from the CNN (the system using machine learning model looks into the encoded letters
for high usage in the consecutive occurrences of the timeseries power consumption
data to identify a suspected EV charging event, [0057]-[0059] and [0092]).
Regarding claim 4 combination of Lu et al. and Yan et al. teach the method of
claim 1. In addition Yan et al. teaches, wherein the machine-learned model
comprises a convolutional neural network (CNN) (neural network model used by
machine learning model to differentiate between different power signatures, [0021 ],
[0053], [0054]), and wherein the CNN (neural network in view of Yan et al.) is trained
on input comprising (machine learning technique or model is trained with defined
(labelled) signatures of different power events, [0025] and [0026]).
In addition Lu et al teaches, labeled data based on EV charging events (the
machine learning classifier is trained with EV charging patterns that is labelling EV data,
[0033], [0102] and [0103]); and
labeled data based on HVAC operational events (the machine learning
classifier is also trained with non EV charging patterns which can include load for
weather related HVAC usage, [0003], [0102] and [0103]).
Regarding claim 7 combination of Lu et al. and Yan et al. teach the method of
claim 1. In addition Lu et al. teaches, wherein:
the likelihood value comprises a value from 0.0 to 1.0 (if the assigned
probability of a suspected EV charging event is greater than 0.5, than the suspected
EV charging event is an actual EV charging event. That is the probability of suspected
EV charging event is between 0-1, [0084]); and
the time-range is one week (data is collected for 30 days. To a person of
ordinary skill in the art any range of time intervals can be selected based on system
needs. Also there is no teaching on the reference that data can only be collected for 30
days not for any other time period, [0040] and [0042]).
Regarding claim 15, combination of Lu et al. and Yan et al. teach the claimed
method of detecting EV charging. Together they teach the computing device (computing
device as taught in [0109] and [011 0] of Lu et al.) performing the functional limitations of detecting EV charging and therefore rejected for the reasons discussed above in claim 1. Claim 15 has additional limitation taught by Lu et al. that is, one or more non-transitory computer-readable media storing computer-executable instructions
that, when executed by one or more processors (EV detection system and the
computer is means structure hardware, non-transitory computer readable
medium performing computer executable instructions by processor ([0111] and
[0112], Lu et al.).
Claims 16-18 recite functional limitations similar to claims 2-4. Therefore combination of Lu et al. and Yan et al. teach the claims 16-18 and therefore rejected under 35 U.S.C.103 for the reasons discussed above in claims 2-4.
Claim(s) 5 and 19 are rejected under 35 U.S.C.103 as being unpatentable over
Lu et al. (US 20200122598 A1) in view of Yan et al. (US 20210088563 A1) in further
view of Franklin et al. (US 20190295190 A1).
Regarding claim 5, combination of Lu et al. and Yan et al. teach the method of
claim 1. In addition Lu et al. teaches, distinguishing the change in consumption
from an EV charging event (" ... The charge features will help the system learn what an EV charge looks like and what an EV charge does not look like so that an EV charge
can be distinguished ... ", [0066] from other non EV charging events, [0023]).
Neither in combination nor individually Lu et al. and Yan et al. teach the details of
determining a change in temperature data coincident with terms in the time-series of
consumption-change data indicating an increase in electricity consumption. However
both Lu et al. and Yan et al. look at different power usage data to identify specific power
events such as EV charging.
Franklin et al. teaches, determining a change in temperature data coincident
with terms in the time-series of consumption-change data indicating an increase
in electricity consumption (an inferential model learns multiple loadshapes
(consumption change data) to identify events for fluctuation in power consumption due
to variation in real-time weather, vehicle charging and other events. Real time weather
affects the ambient temperature thus causes change in electrical consumption or
loadshapes that is temperature data coincident with consumption change data
indicating variation that is indicating increase in electricity consumption, [0031] and
[0013]).
Franklin et al. is an analogous art in combination with Lu et al. and Yan et al.
because is from similar problem solving area that is analyzing electrical consumption
data to predict or identify power events based on electrical power usage data analysis.
Therefore it would have been obvious before the effective filing date of the
claimed invention to a person of ordinary skill in the art to modify the method of
detecting Ev charging events by distinguishing the change in consumption from an EV
charging event as taught by Lu et al. and Yan et al. by applying the known technique of
determining change in temperature data such as change in ambient temperature due to
change in weather and thus detecting variation in electrical consumption as taught by
Franklin et al. as an improvement to the method of detecting EV charging event to yield
predictable results for distinguishing between EV charging events and other power
usage events caused by change in temperature.
Regarding claim 19, combination of Lu et al. and Yan et al. teach the one or
more computer-readable media of claim 15. In addition Lu et al. teaches, distinguishing the change in consumption from an EV charging event (" ... The
charge features will help the system learn what an EV charge looks like and what an EV
charge does not look like4 so that an EV charge can be distinguished ... ", [0066] from
other non EV charging events, [0023]).
Neither in combination nor individually Lu et al. and Yan et al. teach the details of
determining a change in temperature data coincident with terms in the time-series of
consumption-change data indicating an increase in electricity consumption. However both Lu et al. and Yan et al. look at different power usage data to identify specific power
events such as EV charging.
Franklin et al. teaches, determining a change in temperature data coincident
with terms in the time-series of consumption-change data indicating an increase
in electricity consumption (an inferential model learns multiple loadshapes
(consumption change data) to identify events for fluctuation in power consumption due
to variation in real-time weather, vehicle charging and other events. Real time weather
affects the ambient temperature thus causes change in electrical consumption or
loadshapes that is temperature data coincident with consumption change data
indicating variation that is indicating increase in electricity consumption, [0031] and
[0013]).
Franklin et al. is an analogous art in combination with Lu et al. and Yan et al.
because is from similar problem solving area that is analyzing electrical consumption
data to predict or identify power events based on electrical power usage data analysis.
Therefore it would have been obvious before the effective filing date of the
claimed invention to a person of ordinary skill in the art to modify the method of
detecting Ev charging events by distinguishing the change in consumption from an EV
charging event as taught by Lu et al. and Yan et al. by applying the known technique of
determining change in temperature data such as change in ambient temperature due to
change in weather and thus detecting variation in electrical consumption as taught by
Franklin et al. to yield predictable results of detecting EV charging event and distinguishing between EV charging events and other power usage events caused by change in temperature.
Claim(s) 8-12 and 14 are rejected under 35 U.S.C.103 as being unpatentable over Lu et al. (US 20200122598 A1) in view of Yan et al. (US 20210088563 A1) in further view of Linchieh et al. (US 20240146060 A1).
Regarding claim 8 Lu et al. teaches, a computing device to detect electric
vehicle (EV) charging events (computing device including a hardware processor and a
memory, [0109] and [011 0]), wherein the computing device comprises:
a processor (hardware processor 510, [011 0]); and
a memory device (memory 515, [011 0]), in communication with the
processor (memory in communication with processor, [0109] and [011 0]), wherein the
memory device comprises statements executed by the processor to perform
actions (computer including memory and processor execute instructions, firmware and
or combinations, [0109]-[0111]) comprising:
obtaining a time-series of electrical-consumption data of a service
site over a time-range (obtaining time series electricity consumption data from a
premise or location (service site) for a period of time, [0024], [0026] and [0040]);
converting the time-series of electrical-consumption data into a
time-series of consumption-change data (by analyzing the electricity consumption
data in conjunction to EV charging activity data over the period of time, consumption
data related to EV charging event or consumption change can be determined for the
obtained time series electricity consumption data, [0040], [0041], [0043],[0062] and see
also [0044] reciting pre-processing raw KWh data particular magnitude and variance
(consumption change) over a period of time);
providing the input data over the time-range to a subroutine configured to
detect EV charging incidents (pre-processed data is fed into the machine learning
model to determine EV charging event by analyzing the obtained and processed
electricity consumption data, [0092] and [0093]), wherein the subroutine performs
actions comprising: identifying load-changes within the time-range, wherein the load-changes are of a magnitude to indicate EV charging (the trained machine learning model analyze the processed electricity consumption data based on certain conditions of charging such as magnitude of the initial sharp jump in kWh when the suspected EV charge starts, a magnitude of the drop in kWh when the suspected EV charge ends, a duration of the suspected EV charge event, a standard deviation of kWh values during the suspected EV charge, a total kWh usage during the suspected EV charge, a maximum and/or minimum kWh value during the suspected EV charge. After analysis the trained machine learning model outputs a value indicating EV charging events, [0084] and [0092]-[0094], see also [0059]); and
responsive to the subroutine recognizing one or more possible EV
charging incidents (" ... The match may be based on comparing corresponding types of charge features and determining if the values match to a certain degree (e.g., to a set
threshold amount). For example, the classifier determines whether the average
consumption of electricity that was used during the suspected EV charge matches the
average consumption of electricity that was used during a known EV charge event from
a matching EV charge pattern/motif. .. ", [0093]), determining a likelihood of at least
one EV charging event during the time-range based at least in part on the one or more possible EV charging events (Suspected EV charge events with an assigned
probability greater than 0.5 is classified as EV charges [0084] and [0092]-[0094], see
also [0059]); and
responsive to detection of an EV charging event performing at least one of: changing a transformer used by the service site (based on identified EV
charging events, corrective actions can be taken by rerouting power flow from a
different transformer, replacing or resizing the transformer for higher capacity, [0016]
and [0099]);
changing a number of service sites served by the transformer (based on
identified EV charging events, corrective actions can be taken by rerouting power flow
from a different transformer, replacing or resizing the transformer for higher capacity,
[0016] and [0099]); or
changing a phase of electricity provided to the transformer.
Lu et al. does not teach the details of associating temperature data with terms of
the time-series of consumption-change data to thereby create input data over the time-range and filtering the load-changes to remove load-changes coincident with
temperature changes. Lu et al. teaches to distinguish between EV charging events and
non- EV charging events which could be related to HVAC usage as mentioned in
[0003]. But Lu et al. does not remove any load changes data but rather keeps and processes all the load changes data and encode them to identify which load change
data is for EV charging and which load change is non EV charging event as taught in
[0042] and [0066].
Yan et al. teaches, associating temperature data with terms of the timeseries
of consumption-change data to thereby create input data over the timerange
(time series data or power measurements from PMU are obtained followed by
FTT transformation for consumption change data and weather data where the PMU is
located is also obtained for a particular time window. That is associating temperature
data (ambient temperature from weather data) with consumption change data
[0023],[0053] and [0059]).
Lu et al. and Yan et al. are analogous art because that are from similar problem
solving area of distinguishing between different power events based on analyzing
electrical power consumption data.
Therefore it would have been obvious before the effective filing date of the
claimed invention to a person of ordinary skill in the art to modify the system detecting
EV charging event based on analyzing electrical consumption data and consumption
change data by applying the known technique of associating ambient temperature data
to consumption change data as taught by Yan et al. as an improvement to data
processing of the electrical consumption data to yield predictable results for
distinguishing between EV charging events and other events such as weather related
HVAC usage as mentioned by Lu et al. in [0003].
Neither in combination nor individually Lu et al. and Yan et al. teach the details of
filtering the load-changes to remove load-changes coincident with temperature
changes.
Linchieh et al. teaches, filtering the load-changes to remove load-changes
coincident with temperature changes (data from multiple sources are collected as
taught in [0091] and [0093]. Sources include grid data, historical, real time and predicted
weather data, and others as taught in [0093]). The collected data is preprocessed to
remove outliers which can include excessive variation in power usage due to seasonal
trends and many other factors as taught in [0169] and feed the processed data to the
predictor to predict load usage in near real time, [0085] and [0086]).
Linchieh et al. is an analogous art because it is in the similar problem solving
area which is analyzing collected electrical consumption data to determine or predict EV
charging events and other power consumption events.
Therefore it would have been obvious before the effective filing date of the
claimed invention to a person of ordinary skill in the art to modify the load changes within the time range as taught by combination Lu et al. and Yan et al. by applying the
known technique of removing outlier data such as variation in usage due to seasonal or
weather pattern (that is change in temperature) during data preprocessing as taught by
Liechieh et al. as an improvement to load-changes data to yield predictable results for
determining particular power events which will help to optimizing energy usage to
minimize overall peak demand for the power grid as taught by Liechieh et al. in [0109].
Regarding claim 9 combination of Lu et al., Yan et al. and Liechieh et al. teach
the computing device of claim 8. In addition Lu et al. teaches, wherein identifying
load-changes within the time-range by the subroutine comprises:
recognizing a change in consumption between two terms in the timeseries
of consumption-change data (the trained machine learning model can identify the
start and stop time for EV charging event (change in consumption between two terms)
by analyzing the collected kWh data and comparing it with known EV charge event
features (such as kWh level remains at that high level for a sustained period of time and
then drops thus the subsequent term in timeseries data will be much less than previous
kWh data). Then the identified/suspected EV charge event is compared to a set
threshold as a confirmation for EV charge event, [0063],[0064] and [0093]), wherein the
change in consumption is 3 kWh or more (consumption change data between 0-3 or
0-10 KWh, [0050]).
Regarding claim 10 combination of Lu et al., Yan et al. and Liechieh et al. teach
the computing device of claim 8. In addition Yan et al. teaches, wherein the subroutine comprises a convolutional neural network (CNN) (neural network model
used by machine learning model to differentiate between different power signatures,
[0021], [0053], [0054]), and wherein the subroutine performs additional actions
comprising.
In addition Lu et al. teaches, determining weights to associate with terms m
the time-series of consumption-change data (" ... Each kWh value in each time
interval is encoded with one of the encoded letters5 based on the usage level as
discussed. A resulting encoding string 325 for the series of time intervals of the account
data 300 is shown below the graph of FIG. 3. Each encoded letter of the encoded string
325 is positioned in an approximate alignment below its corresponding kWh value and
time interval ... ", [0048] and [0050]); and
using the weights associated with a plurality of terms to derive an output
from the CNN (the system using machine learning model looks into the encoded letters
for high usage in the consecutive occurrences of the timeseries power consumption
data to identify a suspected EV charging event, [0057]-[0059] and [0092]).
Regarding claim 11 combination of Lu et al., Yan et al. and Liechieh et al. teach
the computing device of claim 8. In addition Yan et al. teaches, wherein the
subroutine comprises a convolutional neural network (CNN) (neural network model
used by machine learning model to differentiate between different power signatures,
[0021], [0053], [0054]), and wherein the CNN is trained on input comprising (machine learning technique or model is trained with defined (labelled) signatures of
different power events, [0025] and [0026]).
In addition Lu et al teaches, labeled data based on EV charging events (the
machine learning classifier is trained with EV charging patterns that is labelling EV data,
[0033], [0102] and [0103]); and
labeled data based on HVAC operational events (the machine learning
classifier is also trained with non EV charging patterns which can include load for
weather related HVAC usage, [0003], [0102] and [0103]).
Regarding claim 12 combination of Lu et al., Yan et al. and Liechieh et al. teach
the computing device of claim 8. In addition Liechieh et al. teaches, determining a
change in temperature data coincident with terms in the time-series of
consumption-change data indicating an increase in electricity consumption
(seasonal trend data will show change in usage due change in temperature due to
change in seasonal weather trend, [0093] and [0169]).
In addition Lu et al. teaches, distinguishing the change in consumption from
an EV charging event (" ... The charge features will help the system learn what an EV
charge looks like and what an EV charge does not look like so that an EV charge can
be distinguished ... ", [0066] from other non EV charging events, [0023]).
Regarding claim 14 combination of Lu et al., Yan et al. and Liechieh et al. teach
the computing device of claim 8. In addition Lu et al. teaches, wherein:
the likelihood value comprises a value from 0.0 to 1.0 (if the assigned
probability of a suspected EV charging event is greater than 0.5, than the suspected
EV charging event is an actual EV charging event. That is the probability of suspected
EV charging event is between 0-1, [0084]); and
the time-range is one week (data is collected for 30 days. To a person of
ordinary skill in the art any range of time intervals can be selected based on system
needs. Also there is no teaching on the reference that data can only be collected for 30
days not for any other time period. [0040] and [0042]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wong et al. (US 20240388100 A1) teaches load distribution system where time load data is collected over a period of time and time series ambient temperature data is also collected over a period of time and each time series load data is paired with time series ambient temperature data as taught in [0038] and [0039]. Specifically Wong et al. recites, “…Example annotations for a load amount for a location for a particular time point may be, for example, {“day type”: “weekday”, “load type”: “residential”, “load coun6t”: 10, “temperature”: 85, “season”: “summer”, “cloud coverage”: “sunny”}…”.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANZUMAN SHARMIN whose telephone number is (571)272-7365. The examiner can normally be reached M and Th 7:00am - 3:00pm and Tue 8:00am-12:00pm.
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/ANZUMAN SHARMIN/Examiner, Art Unit 2115
/KAMINI S SHAH/Supervisory Patent Examiner, Art Unit 2115
1 Consumption change data.
2 Consumption change date in view of Lu et al.
3 Weights to associate power consumption terms in the time series of consumption change data.
4 Non EV charging event.
5 Weights to associate power consumption terms in the time series of consumption change data.
6 Consumption data associated with temperature for that time in the time period.
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