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 .
DETAILED ACTION
2. The action is responsive to the communications filed on 10/17/2025. Claims 1, 3-11, 13, 15-23 are pending in the case. Claims 2, 12, 14 are cancelled. Claims 1, 3, 11, 13, 20 are amended. Claims 21-23 are newly added. Claims 1, 11, 20 are independent claims. Claims 1, 3-11, 13, 15-23 are rejected.
Summary of claims
3. Claims 1, 3-11, 13, 15-23 are pending,
Claims 1, 3, 11, 13, 20 are amended,
Claims 2, 12, 14 are cancelled,
Claims 21-23 are newly added,
Claims 1, 11, 20 are independent claims,
Claims 1, 3-11, 13, 15-23 are rejected,
Remarks
4. Applicant’s arguments, see Remarks, filed on 10/17/2025, with respect to the rejection of claims 1-20 under 101 have been fully considered and withdrawn; with respect to the rejection(s) of claim(s) 1, 3-11, 13, 15-23 under 103 have been fully considered and are not persuasive in view of new rejection ground(s).
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
5. Claim 23 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Specifically, claim 23 recites “determining an initial boundary between the first line segment and the second line segment based on comparison of the second trendline to the first trendline; comparing the initial boundary to a boundary condition”, however, the provided portions of the specification ([0051]-[0052], [0066]-[0067]) did not describe what an initial boundary refers to, and how the initial boundary is compared to a boundary condition.
Claim Rejections - 35 USC § 103
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.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
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.
6. Claims 1, 3-11, 13, 15-21, 23 are rejected under 35 U.S.C. 103 as being unpatentable over Mahnameh Taheri et al (US Publication 20230299611 A1, hereinafter Taheri), and in view of Adrian Albert et al (US Publication 20150332294 A1, hereinafter Albert), and Young Lee et al (US Publication 20200076196).
As for independent claim 1, Taheri discloses: A method for disaggregating seasonal load from overall electricity consumption (Taheri: Abstract, Certain examples described herein provide a system and a method for disaggregating energy load data for a building. The system (410) may have an energy load data interface (420) to receive energy load data (422) originating from energy use sensors for the building; a weather data interface (430) to receive weather data (432) for a location that includes the building… and an energy use disaggregator (460) to process the variable energy use component of the energy load data and determine a plurality of time-varying load components (462) of the energy load data), the method comprising: receiving, by a disaggregation system, historical data comprising (i) historical daily load data for a residence for a particular time period (Taheri: [0038], receive the energy load data 422 from a communicatively coupled memory or storage device (e.g., as historical data); [0080], periods of occupancy are estimated by temporally segmenting the energy load data over a set of predefined time periods. For example, a daily average for each time within a day may be determined (e.g., either for specific named days, so as to generated multiple daily averages for the days of the week, or for all named days)) and (ii) an average temperature at the residence for each day in the particular time period (Taheri: [0040], the energy disaggregation system 410 also receives weather data 432 at the weather data interface 430. The weather data 432 may comprise at least an outdoor temperature for a location that includes the building); fitting, by the disaggregation system, a data model to the historical data (Taheri: [0043], the weather adjustment pre-processor 440 may fit a linear (including multi-linear) model (e.g., a linear or multi-linear regression model) to the weather data), wherein the data model includes a piecewise linear function having a first line segment and a second line segment (Taheri: [0045], a piecewise continuous temperature response model may be used; [0043], the weather adjustment pre-processor 440 may fit a linear (including multi-linear) model (e.g., a linear or multi-linear regression model) to the weather data; [0046], for an input temperature value, there may be six values for linear regression of every time point; [0071], Values between the lower and upper thresholds may then be scaled linearly), and wherein fitting the data model to the historical data comprises: fisting a first trendline to a first subset of the historical daily load values, fitting a second trendline to a second subset of the historical daily load values (Taheri: [0043], the weather adjustment pre-processor 440 may fit a linear (including multi-linear) model (e.g., a linear or multi-linear regression model) to the weather data; [0046], for an input temperature value, there may be six values for linear regression of every time point; [0071], Values between the lower and upper thresholds may then be scaled linearly), … calculating, by the disaggregation system and using the data model, an estimated seasonal load for a target time period (Taheri: [0082], Changes may be predicted using machine learning models (e.g., deep neural networks) and/or based on historic data. In certain cases, the energy disaggregation system 410 may be configured to receive simulated energy load data from a simulated building and to output predicted time-varying load components for the simulated building); and causing, by the disaggregation system, one or more notifications to be provided based on the estimated seasonal load for the target time period (Taheri: [0006], Graphical user interfaces are also presented for the presentation of such data; [0082], Recommendations for control actions that are predicted to reduce the said one or more of the plurality of time-varying load components may be output, e.g. presented to a user via a user interface; please note the estimated data is presented to a user via a user interface).
Taheri discloses disaggregating energy load data based on historical data and weather data but does not clearly discloses the estimated seasonal load, in an analogous art of energy consumption monitoring and managing system, Albert discloses: (Albert: Figs. 6A-D and [0034], illustrate seasonal and time-of-day distribution of occupancy states for two real customers using a graph of count vs hour of day for summer (FIG. 6A) and winter (FIG. 6B) of a first customer and for summer (FIG. 6C) and winter (FIG. 6D) of a second customer; [0109], FIGS. 6A-D present the breakdown of thermal occupancy states for the two example customers by the summer and winter seasons (as defined by PG&E) and by hour-of-day);
Taheri and Albert are analogous arts because they are in the same field of endeavor, energy consumption monitoring and managing system. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Taheri using the teachings of Albert to include estimating seasonal data and presenting to user. It would provide Taheri’s method with enhanced capabilities of allowing user to aware the different energy consumption in different seasons and manage energy consumption.
Further, Taheri discloses boundary and boundary points (Taheri [0072]), but does not disclose determining a boundary based on comparison of data, in an analogous art of energy consumption monitoring and managing system, Lee discloses: comparing the second trendline to the first trendline, and determining a boundary between the first line segment and the second line segment based on comparison of the second trendline to the first trendline (Lee: [0191], One set of capacity constraints may apply to the boundary condition at the end of each time step i, whereas the other set of capacity constraints may apply to the boundary condition at the beginning of the next time step i+1. For example, if a first amount of battery capacity is reserved for frequency regulation during time step i and a second amount of battery capacity is reserved for frequency regulation during time step i+1, the boundary point between time step i and i+1 may be required to satisfy the capacity constraints for both time step i and time step i+1; [0271], Comparator 1106 can be configured to receive a segment of occupancy data from data parser 1104 and determine whether occupancy has increased or decreased by a predefined amount over a period of time; [0273], Comparator 1106 can be configured to compare the segment of data accordingly);
Taheri and Lee are analogous arts because they are in the same field of endeavor, energy consumption monitoring and managing system. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Taheri using the teachings of Lee to include applying boundary condition and boundary points. It would provide Taheri’s method with enhanced capabilities of allowing user to have a better idea how to manage energy consumption.
claims 2, 12, 14 cancelled
As for claim 3, Taheri-Albert-Lee discloses: wherein the piecewise linear function has the first line segment, the second line segment, and a third line segment, one of which estimates a baseline daily load for the residence (Taheri: Fig. 1; Taheri: [0059], The temporal processing engine 614 is configured to segment and aggregate the energy load data over a set of predefined time periods; Albert: Fig. 2A, [0059], this model is presented in FIG. 2A. FIG. 2B illustrates the temperature dependency of the temperature response rate implied by the model, i.e., a piecewise constant profile with three response regimes (a.sub.C<0, 0, and a.sub.H>0)).
As for claim 4, Taheri-Albert-Lee discloses: wherein the disaggregation system applies segmented regression to fit the data model to the historical data (Taheri: [0043], the weather adjustment pre-processor 440 may fit a linear (including multi-linear) model (e.g., a linear or multi-linear regression model) to the weather data; [0075], As well as the linear regression models described herein, other more complex models may also be applied. These include Fourier Series or spectral models and Gaussian process regression).
As for claim 5, Taheri-Albert-Lee discloses: wherein the target time period is within the particular time period (Taheri: [0023], electrical power or gas consumption may be recorded at predefined time intervals (e.g., every minute, every defined fraction of an hour or hourly) by sensor devices), is outside the particular time period, or overlaps the particular time period (Taheri: [0059], the data partition engine 612 is configured to apply a clustering model to the energy load data 422 to determine one or more partitions within the data. This may then be used to differentially process the energy load data 422 for different partitions or period within the data. The temporal processing engine 614 is configured to segment and aggregate the energy load data over a set of predefined time periods).
As for claim 6, Taheri-Albert-Lee discloses: wherein calculating the estimated seasonal load for the target time period includes: generating, by the disaggregation system, an estimated seasonal load for each day within the target time period (Albert: Figs. 6A-D and [0034], illustrate seasonal and time-of-day distribution of occupancy states for two real customers using a graph of count vs hour of day for summer (FIG. 6A) and winter (FIG. 6B) of a first customer and for summer (FIG. 6C) and winter (FIG. 6D) of a second customer; [0109], FIGS. 6A-D present the breakdown of thermal occupancy states for the two example customers by the summer and winter seasons (as defined by PG&E) and by hour-of-day); and calculating, by the disaggregation system, a sum including the estimated seasonal load for each day within the target time period, wherein the sum comprises the estimated seasonal load for the target time period (Taheri: [0051], the set of energy load data components 472 may be computed as described to sum to the raw energy load data value).
As for claim 7, Taheri-Albert-Lee discloses: wherein calculating the estimated seasonal load for a particular day within the target time period includes: identifying, by the disaggregation system, an average temperature for the particular day (Taheri: [0040], the energy disaggregation system 410 also receives weather data 432 at the weather data interface 430. The weather data 432 may comprise at least an outdoor temperature for a location that includes the building); determining, by the disaggregation system and using the data model and the average temperature, an overall daily load and a baseline daily load (Taheri: Abstract, a baseline adjustment pre-processor (450) to process the weather-independent energy use component of the energy load data and determine a baseline energy use component (456) of the energy load data, wherein the baseline adjustment pre-processor is configured to remove the baseline energy use component from the weather-independent energy use component to determine a variable energy use component (458) of the energy load data); and subtracting, by the disaggregation system, the baseline daily load from the overall daily load to produce the estimated seasonal load for the particular day (Taheri: Abstract, a baseline adjustment pre-processor (450) to process the weather-independent energy use component of the energy load data and determine a baseline energy use component (456) of the energy load data, wherein the baseline adjustment pre-processor is configured to remove the baseline energy use component from the weather-independent energy use component to determine a variable energy use component (458) of the energy load data; [0053], This approach may be used for one or more of lighting and small power loads, with a miscellaneous load being the remainder after lighting and small power loads have been deducted from the variable energy use component; [0071], Values between the lower and upper thresholds may then be scaled linearly (e.g., normalised to lie on a range of between 0 and 1 by subtracting the lower threshold and then by dividing by the difference between the upper and lower thresholds); [0077], the baseline energy use component is removed from the weather-independent energy use component to determine a variable energy use component of the energy load data).
As for claim 8, Taheri-Albert-Lee discloses: adjusting, by the disaggregation system, the estimated seasonal load for the particular day (Taheri: [0064], Each of these data sets may relate to a different time period, e.g. different days of the week or different times of day (if a daily aggregate or average for named days of the week is generated then m=7)).
As for claim 9, Taheri-Albert-Lee discloses: wherein adjusting the estimated seasonal load for the particular day includes: deducting, by the disaggregation system, pool pump electricity use (Taheri: [0007], within a small home or residence, there are a small number of appliances, with often one of each appliance type (e.g., one fridge, cooker, pool pump etc.)) from the estimated seasonal load for the particular day (Taheri: [0053], This approach may be used for one or more of lighting and small power loads, with a miscellaneous load being the remainder after lighting and small power loads have been deducted from the variable energy use component; [0071], Values between the lower and upper thresholds may then be scaled linearly (e.g., normalised to lie on a range of between 0 and 1 by subtracting the lower threshold and then by dividing by the difference between the upper and lower thresholds); [0077], the baseline energy use component is removed from the weather-independent energy use component to determine a variable energy use component of the energy load data; please note pool pump electricity usage may be deducted as miscellaneous load).
As for claim 10, Taheri-Albert-Lee discloses: wherein, in an instance in which an average temperature for the particular day exceeds a threshold (Taheri: [0045], a plurality of thresholds may be defined to create different temperature bands), adjusting the estimated seasonal load for the particular day includes: identifying, by the disaggregation system, a cooling load cap based on a theoretical calculation of seasonal cooling load for the residence (Taheri: [0050], In certain examples, these may additionally comprise a heating, ventilation and air conditioning (HVAC) load component; [0077], The time-varying load components may also comprise other components including a heating, ventilation and air conditioning (HVAC) load component); and in an instance in which the estimated seasonal load for the particular day exceeds the cooling load cap, reducing, by the disaggregation system, the estimated seasonal load for the particular day to the cooling load cap (Taheri: [0053], This approach may be used for one or more of lighting and small power loads, with a miscellaneous load being the remainder after lighting and small power loads have been deducted from the variable energy use component; [0071], Values between the lower and upper thresholds may then be scaled linearly (e.g., normalised to lie on a range of between 0 and 1 by subtracting the lower threshold and then by dividing by the difference between the upper and lower thresholds); [0077], the baseline energy use component is removed from the weather-independent energy use component to determine a variable energy use component of the energy load data).
As per claims 11, 13, 15-19, it recites features that are substantially same as those features claimed by claims 1, 3, 6-10, thus the rationales for rejecting claims 1, 3, 6-10 are incorporated herein.
As per claim 20, it recites features that are substantially same as those features claimed by claim 1, thus the rationales for rejecting claim 1 are incorporated herein.
As for claim 21, Taheri-Albert-Lee discloses: determining a forecasted estimated seasonal load for a new target time period based on the estimated seasonal load for the target time period and the historical data (Taheri: [0082], predicting a change in said one or more of the plurality of time-varying load components in response to one or more of the set of control actions. For example, the control actions may comprise switching off or changing operating parameters for appliances and/or equipment within the building); comparing the forecasted estimated seasonal load to a threshold value (Taheri: [0045], a plurality of thresholds may be defined to create different temperature bands, wherein, if the input temperature value falls within a band (i.e. is above threshold A but below threshold B), then the band temperature factor is set as the input temperature value minus the lower threshold (A), and wherein, if the input temperature value is above the band, a value for the band is set as the band width (e.g. B-A); and control a thermostat based on comparison of the forecasted estimated seasonal load to a threshold value (Albert: [0020], the methodology allows one to compute future expected usage of the temperature-dependent part of consumption for current setting of the HVAC (thermostat setpoint), which can be communicated to the customer either interactively (real-time on-device) or through their on-line or off-line electricity bill. With a change in the setting of the thermostat).
As for claim 23, Taheri-Albert-Lee discloses: determining the boundary between the first line segment and the second line segment based on comparison of the second trendline to the first trendline includes: determining an initial boundary between the first line segment and the second line segment based on comparison of the second trendline to the first trendline; comparing the initial boundary to a boundary condition; and determining the boundary between the first line segment and the second line segment as a boundary condition threshold value in response to the initial boundary violates the boundary condition (Lee: [0191], One set of capacity constraints may apply to the boundary condition at the end of each time step i, whereas the other set of capacity constraints may apply to the boundary condition at the beginning of the next time step i+1. For example, if a first amount of battery capacity is reserved for frequency regulation during time step i and a second amount of battery capacity is reserved for frequency regulation during time step i+1, the boundary point between time step i and i+1 may be required to satisfy the capacity constraints for both time step i and time step i+1).
7. Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Taheri, Albert and Lee as applied on claim 1, and further in view of Craig Howard Miller (US Publication 20060276938 A1, hereinafter Miller).
As for claim 22, Taheri-Albert-Lee does not clearly disclose a correlation coefficient R-squared, in another analogous art of optimizing the control of energy supply and demand, Miller discloses: determining the boundary between the first line segment and the second line segment based on comparison of the second trendline to the first trendline includes determining an R-squared coefficient or slope of the first trendline varies from an R-squared coefficient or slope of the second trendline by a predefined amount (Miller: [0162], A correlation coefficient (r-squared) is calculated, (1304) and the best match is identified (1305). This pattern is then scaled up or down linearly to reflect the absolute level of energy use (1306) as described below. The final step is to update the pattern to reflect actual energy use (1307). A simple method for accomplishing this is described below. An alternative method is to use a Bayesian approximation);
Taheri and Albert and Lee and Miller are analogous arts because they are in the same field of endeavor, energy consumption monitoring and managing system. Therefore, it would have been obvious to one with ordinary skill, in the art before the effective filing date of the claimed invention, to modify the invention of Taheri using the teachings of Lee to include a correlation coefficient R-squared. It would provide Taheri’s method with enhanced capabilities of optimizing the control of energy supply and demand.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hua Lu whose telephone number is 571-270-1410 and fax number is 571-270-2410. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached on 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 703-273-8300.
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/Hua Lu/
Primary Examiner, Art Unit 2118
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