Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Application
Claim 1-15 have been examined in this application. This communication is the first action on the merits.
Drawings
3. The drawings filed on 10/18/24 are acceptable for examination proceedings.
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.
4. Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more as fully discussed below.
5. Regarding Independent claim 1, and 15:
Step 1: Yes
Claim 1 is drawn to an artificial intelligence apparatus and claim 15 is drawn to a method, therefore claim 1, and 15 falls under one of four categories of statutory subject matter (process/method, machines/products/apparatus, manufactures, and compositions of matter).
Step 2A, Prong 1: Yes
Independent claim 1, and 15 are directed to a judicially recognized exception of an abstract idea without significantly more.
Claim 1, and 15 recites limitation of “checks whether there is a specific electronic device for which the power consumption data has not been obtained, and if there is a specific electronic device for which the power consumption data has not been obtained, classifies the obtained mixed power data into a plurality of individual power data, estimates power consumption data for the specific electronic device based on the extracted individual power data, and predicts an amount of power consumption in a home based on power consumption data for all pre-registered electronic devices” that under their broadest reasonable interpretation, enumerates a mental concept. A human can mentally perform the claimed function of checking, estimating and predicting as discussed above. Thus, these claimed functions are the judicial exceptions that are no more than a mental abstract idea (See MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2: No
Claim 1, and 15 recites additional limitation of “obtains power consumption data for pre-registered electronic devices; obtains mixed power data consumed in a current home …..; extracts individual power data matching the specific electronic device”. The claimed function of obtains power consumption data, and extracts individual power data is forms of insignificant input or output solution activities (i.e., extra solution), such that obtaining of data is necessary for the use of the judicial exception (See MPEP 2106.05(g)). The combination of these additional elements does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 1 further recite additional limitation of " a memory storing power consumption data; and a processor predicting power consumption in a home";
Claim 15 further recite additional limitation of “a processor” are considered as do not integrate into practical application and are recited at a high level of generality such that amount to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.95(f)).
The combination of these additional elements does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B: No
The additional limitation that are a form of insignificant extra-solution activities, do not amount to significantly more than an abstract idea because the court decisions have determined that this additional element as discussed above in step 2A of obtains power consumption data to be well-understood, routine, and conventional when claimed in a merely generic manner for data receiving (i.e., acquiring, and collecting) (See MPEP § 2106.05(d)(II) (i: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)).
As such, claim 1, and 15 are not patent eligible.
6. Dependent claims 2-14:
Step 1: Yes
Claim 2-14 are drawn to an artificial intelligence apparatus, therefore claim 2-14 are falls under one of four categories of statutory subject matter (process/method, machines/products/apparatus, manufactures, and compositions of matter).
Step 2A, Prong 1: Yes
Dependent claim 2-14 are directed to a judicially recognized exception of an abstract idea without significantly more.
Claim 7-9, and 11-13 recites limitation which includes “a pre-learned neural network model” is fundamentally a mathematical concept made of numbers and equations.
The claimed pre-learned neural network model under their broadest reasonable interpretation, includes mathematical formulas and equations and hence enumerates a mathematical concepts.
Thus, these claimed functions are the judicial exceptions that are no more than an abstract idea processed by a mathematical concepts (See MPEP 2106.04(a)(2)(I)).
Claim 4 further recites limitation of “when checking whether there is a specific electronic device for which the power consumption data has not been obtained, matches the obtained power consumption data to the pre-registered electronic device, and checks whether the power consumption data has been matched to all of the pre-registered electronic devices to check whether there is a specific electronic device for which the power consumption data has not been obtained”;
Claim 5 further recites limitation of “recognizes an electronic device among the pre-registered electronic devices in which a communication connection is not performed as a specific electronic device from which the power consumption data is not received”;
Claim 10 further recites limitation of “when estimating power consumption data for the specific electronic device, estimates power consumption data including energy information and usage pattern information for the specific electronic device based on individual power data having a specific power consumption waveform” that under their broadest reasonable interpretation, enumerates a mental concept. A human can mentally perform the claimed different determining functions as discussed above. Thus, these claimed functions are the judicial exceptions that are no more than a mental abstract idea (See MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2: No
Claim 2 recites additional limitation of “when obtaining power consumption data for the pre-registered electronic device, obtains power consumption data including energy information and usage pattern information for the pre-registered electronic device”;
Claim 3 recites additional limitation of “and obtains the power consumption data for the pre-registered electronic device from the memory”;
Claim 6 recites additional limitation of “when obtaining mixed power data consumed in the current home, obtains mixed power data in which power consumption waveforms of electronic devices consuming power at the current point in time in the home are mixed”;
Claim 7 recites additional limitation of “and extracts individual power data matching the specific electronic device from among the classified plurality of individual power data
Claim 14 recites additional limitation of “when predicting the amount of power consumption in a home, generates prediction result information based on an amount of the predicted power consumption in a home”;
Claim 7, 11 and 13 recites additional limitation of data input to a pre-learned model.
The claimed function of obtains, and extracts of data and generate result is forms of insignificant input or output solution activities (i.e., extra solution), such that obtaining of data, and generates results are necessary for the use of the judicial exception (See MPEP 2106.05(g)). The combination of these additional elements does not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Claim 3 further recite additional limitation of " the memory";
Claim 2-7, 10-11, and 13-14 further recite “the processor”, which are considered as do not integrate into practical application and are recited at a high level of generality such that amount to no more than mere instructions to apply the exception using a generic computer component (MPEP 2106.95(f)).
Step 2B: No
The additional limitation that are a form of insignificant extra-solution activities, do not amount to significantly more than an abstract idea because the court decisions have determined that this additional element as discussed above in step 2A to be well-understood, routine, and conventional when claimed in a merely generic manner for obtaining data and data outputting (i.e., generates result) (See MPEP § 2106.05(d)(II) (i: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (See Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016)).
As such, dependent claim 2-14 are not patent eligible.
Claim Rejections - 35 USC § 103
7. 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.
8. Claim 1-6, 10, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hyun (KR102471473B1) in view of Takahashi (JP2017017777A).
9. Regarding claim 1, Hyun teaches An artificial intelligence apparatus comprising: a memory storing power consumption data for electronic devices in a home; and a processor predicting power consumption in a home based on the power consumption data, wherein the processor obtains power consumption data for pre-registered electronic devices (e.g., Accordingly, in a preferred embodiment of the present invention, each electronic device has a
certain power consumption pattern according to the registered electronic device and each
registered outlet, and these information is stored in the database (120).) (Para. [0066]),
and predicts an amount of power consumption in a home based on power consumption data for all pre-registered electronic devices (e.g., Subsequently, the system control unit (140) compares the power consumption pattern calculated by the power consumption pattern analysis unit (130) in step S110 with the previous power consumption pattern information stored in the database (120) or the power consumption pattern information retrieved in steps S160 and S165 for each of the multiple outlets (320), calculates the monthly estimated power consumption per outlet for each outlet (320), calculates the total monthly estimated power consumption based on the sum of the monthly estimated power consumption per outlet, and calculates the monthly estimated electricity bill for each selected candidate rate plan based on the total monthly estimated power consumption and the billing standard rate information stored in the database (120) (S170)) (Para. [0092]).
Hyun does not specifically teach checks whether there is a specific electronic device for which the power consumption data has not been obtained, and if there is a specific electronic device for which the power consumption data has not been obtained, obtains mixed power data consumed in a current home, classifies the obtained mixed power data into a plurality of individual power data, extracts individual power data matching the specific electronic device.
Takahashi teaches checks whether there is a specific electronic device for which the power consumption data has not been obtained, and if there is a specific electronic device for which the power consumption data has not been obtained, estimates power consumption data for the specific electronic device based on the extracted individual power data (e.g., The control unit 33 compares the measured current waveform, which is the measurement result of the current of the target circuit that is the subject of measurement, with the registered current waveform, which is the current waveform of the electrical equipment connected to the target circuit, to determine whether the home gateway device 7 cannot acquire the equipment identifier for the target circuit, or whether other unregistered electrical equipment is connected to it (step S33). In other words, the control unit 33 compares the measured current waveform with the registered current waveform, which is the current waveform of electrical equipment 9A connected to the target circuit and whose equipment identifier and operating state can be recognized, to determine whether an unregistered electrical equipment 9B whose equipment identifier and operating state cannot be recognized is connected, or whether an unregistered electrical equipment 9A is connected to which the equipment identifier and operating state can be obtained.) (Para. [0063]), obtains mixed power data consumed in a current home, classifies the obtained mixed power data into a plurality of individual power data, extracts individual power data matching the specific electronic device (e.g., If it is determined that other electrical equipment is connected to the target circuit (step S33, YES), the control unit 33 subtracts the registered current waveform from the measured current waveform to estimate the unregistered current waveform (step S34).
In other words, the control unit 33 can estimate an unregistered current waveform, which is the waveform of the current flowing through an electrical device 9B that is not compatible with HEMS or an electrical device 9A that is not registered in the registration information, connected to the target circuit, by subtracting the registered current waveform of the electrical device 9A that is compatible with HEMS from the measured current waveform. Waveform 43 in Figure 9 shows an example of an unregistered current waveform) (Para. [0064, also refer to Para. [0070]).
Because Takahashi is also directed to a power management, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Hyun and Takahashi before him/her, to modify the teachings of Hyun to include the teaching of Takahashi in order to provide a more convenient power management device, power management system, and power management method and recognize that electrical equipment that is not compatible with HEMS is connected to that circuit (Takahashi: Para. [0003], and [0005]).
10. Regarding claim 2, the combination of Hyun and Takahashi teaches the artificial intelligence apparatus of claim 1, wherein Hyun further teaches the processor, when obtaining power consumption data for the pre-registered electronic device, obtains power consumption data including energy information and usage pattern information for the pre-registered electronic device (Refer to the Para. [0065-[0067]).
11. Regarding claim 3, the combination of Hyun and Takahashi teaches the artificial intelligence apparatus of claim 1, wherein Hyun further teaches the processor, when obtaining power consumption data for the pre-registered electronic device, performs a communication connection with the pre-registered electronic device, stores the power consumption data received from the electronic device connected to the communication in the memory, and obtains the power consumption data for the pre-registered electronic device from the memory (Refer to the Para. [0065-[0067]).
12. Regarding claim 4, the combination of Hyun and Takahashi teaches the artificial intelligence apparatus of claim 1, wherein Takahashi further teaches the processor, when checking whether there is a specific electronic device for which the power consumption data has not been obtained, matches the obtained power consumption data to the pre-registered electronic device, and checks whether the power consumption data has been matched to all of the pre-registered electronic devices to check whether there is a specific electronic device for which the power consumption data has not been obtained (e.g., The control unit 33 compares the measured current waveform with the registered current waveform, which is the current waveform of electrical equipment 9A connected to the target circuit and whose equipment identifier and operating state can be recognized, to determine whether or not electrical equipment 9B that cannot be recognized for its equipment identifier and operating state, or that is not registered in the registration information, is connected. Furthermore, the control unit 33 can estimate an unregistered current waveform, which is the waveform of the current flowing through an electrical device 9B connected to the target circuit that is not compatible with HEMS, or an unregistered electrical device 9B, by subtracting the registered current waveform from the measured current waveform) (para. [0048]).
13. Regarding claim 5, the combination of Hyun and Takahashi teaches the artificial intelligence apparatus of claim 1, wherein Takahashi further teaches the processor recognizes an electronic device among the pre-registered electronic devices in which a communication connection is not performed as a specific electronic device from which the power consumption data is not received (e.g., The determination unit 36 compares the measured current waveform with the registered current waveform to determine whether or not a second electrical device from which device information cannot be acquired is connected to the target circuit) (Para. [0047]).
14. Regarding claim 6, the combination of Hyun and Takahashi teaches the artificial intelligence apparatus of claim 1, wherein Takahashi further teaches the processor, when obtaining mixed power data consumed in the current home, obtains mixed power data in which power consumption waveforms of electronic devices consuming power at the current point in time in the home are mixed (e.g., Furthermore, the control unit 23 calculates the power consumption of the electrical equipment 9B connected to the target circuit based on the unregistered current waveform. If multiple electrical devices 9B are connected to the target circuit, the total power consumption of all electrical devices 9B connected to the target circuit will be calculated) (Para. [0056]).
15. Regarding claim 10, the combination of Hyun and Takahashi teaches the artificial intelligence apparatus of claim 1, wherein Hyun further teaches the processor, when estimating power consumption data for the specific electronic device, estimates power consumption data including energy information and usage pattern information for the specific electronic device calculates the monthly estimated power consumption per outlet for each outlet (320), calculates the total monthly estimated power consumption based on the sum of the monthly estimated power consumption per outlet, and calculates the monthly estimated electricity bill for each selected candidate rate plan based on the total monthly estimated power consumption and the billing standard rate information stored in the database (120) (S170)) (Para. [0092]) [based on individual power data having a specific power consumption waveform].
Hyun does not specifically teach the individual power data having a specific power consumption waveform.
Takahashi teaches the individual power data having a specific power consumption waveform (e.g., As shown in Figure 8, the control unit 33 of the server 3 acquires the device identifier, operating status, and measured current waveform from the home gateway device 7 (step S31). The waveform 41 in Figure 9 shows an example of a measured current waveform. The measured current waveform is a composite waveform of the current waveforms of the electrical equipment connected to the target circuit. To calculate the power consumption of each electrical device, it is necessary to obtain the current waveform for each device from the measured current waveform) (Para. [0061]).
16. Regarding claim 14, the combination of Hyun and Takahashi teaches the artificial intelligence apparatus of claim 1, wherein Hyun further teaches the processor, when predicting the amount of power consumption in a home, generates prediction result information based on an amount of the predicted power consumption in a home (e.g., In addition, according to the present invention, by analyzing the power consumption pattern of each outlet (electronic device) in the home on a unit-by-unit basis, the impact on the current month's electricity bill is accurately predicted based on the usage characteristics of each electronic device by purpose, the usage characteristics of each electronic device by season, and the usage characteristics of each electronic device by time, thereby enabling the user to accurately recognize the impact of each electronic device on the electricity bill and utilize the electronic device according to their intended use) (Para. [0034], also Para. [0092]).
17. Regarding claim 15, Claim 15 recites a method for predicting power consumption of an artificial intelligence apparatus that implement an artificial intelligence apparatus of claim 1, with substantially the same limitations, respectively. Therefore the rejection applied to claim 1, also applies to claim 15 respectively.
18. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Hyun in view of Takahashi, and further in view of Hong (KR20200068070A).
19. Regarding claim 7, the combination of Hyun and Takahashi teaches the artificial intelligence apparatus of claim 1 but does not specifically teach wherein the processor, when extracting individual power data matching the specific electronic device, inputs the obtained mixed power data into a pre-learned neural network model to classify the obtained mixed power data into a plurality of individual power data, and extracts individual power data matching the specific electronic device from among the classified plurality of individual power data.
Hong teaches wherein the processor, when extracting individual power data matching the specific electronic device, inputs the obtained mixed power data into a pre-learned neural network model to classify the obtained mixed power data into a plurality of individual power data, and extracts individual power data matching the specific electronic device from among the classified plurality of individual power data (e.g., The present invention relates to a home appliance classification system and method that measures the amount of power consumed collected from a meter connected to a main power line of a household using various home appliances, extracts individual power consumption patterns of connected sub-devices within the household, and determines whether individual sub-devices are in use at specific points in time in succession. More specifically, the invention relates to a home appliance classification system and method that can improve the accuracy of recognizing whether a sub-device is operating by removing noise from the measured power consumption data and applying a deep learning technique optimized for time-series data.) (Para. [0001], also refer to Para. [0009], [0011]).
Because Hong is also directed to System and method for classifying household appliances from total power consumption data, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Hyun, Takahashi and Hong before him/her, to modify the combined teachings of Hyun, and Takahashi to include the teaching of Hong in order to improve the accuracy of recognizing whether a sub-device is operating by removing noise from the measured power consumption data and applying a deep learning technique optimized for time-series data (Hong: Para. [0001]).
20. Claim 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Hyun in view of Takahashi, further in view of Hong, and further in view of Ichikawa (JP2004297854).
21. Regarding claim 8, the combination of Hyun, Takahashi, and Hong teaches the artificial intelligence apparatus of claim 7 but does not specifically teach wherein the neural network model is pre-learned with power data of an electronic device having a specific power consumption waveform and energy information of the corresponding electronic device as input values.
Ichikawa teaches wherein the neural network model is pre-learned (e.g., Figure 5 shows a method for predicting the amount of electricity used dW. Here, we assume that this neural network has already been trained using past information (actual data and predictive data) as described above, and that the connection weight coefficients 57P and 58P within the neural network have been adjusted. [0030] Actual power information 10a such as the total amount of electricity used within the power receiving equipment and the amount of electricity used by major equipment, process parameter information 20a, and surrounding environment parameters 30 at time Tc are input into the neural network, and predictions are made for this information at time Tc+1, which is the next monitoring cycle. [0031] The neural network inputs the various pieces of information at the predicted time Tc+1 back into the neural network and predicts these pieces of information at the next monitoring cycle, Tc+2.) (Para. [0029]-[0031]) with power data of an electronic device having a specific power consumption waveform and energy information of the corresponding electronic device as input values (e.g., Figure 1 is an overall system configuration diagram of a demand monitoring system showing one embodiment of the present invention. The power input unit 10 of the demand monitoring system 1 uses the energy pulse signal 2 from the energy meter 2 to obtain actual power information 10a of the electrical equipment to be monitored, and provides the actual power information 10a to the integration processing unit (energy calculation unit) 40 and the learning prediction function unit 50 in real time. On the other hand, the process parameter input unit 20 receives equipment/device operating status signals 3a from the equipment/device 3, i.e., the state in which the circuit breaker is connected (closed state) or disconnected (off state), as well as signals such as room temperature, compressor rotation speed, and discharge pressure, and provides these as process parameter information 20a to the learning and prediction function unit 50 in real time.) (Para. [0012]-[0015]).
Because Ichikawa is also directed to demand monitoring system, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Hyun, Takahashi, Hong and Ichikawa before him/her, to modify the combined teachings of Hyun, Takahashi, and Hong to include the teaching of Ichikawa in order to improve the prediction accuracy of this predicted power consumption value, the prediction is performed by learning from actual data (Ichikawa: Para. [0016]).
22. Regarding claim 9, the combination of Hyun, Takahashi, and Hong teaches the artificial intelligence apparatus of claim 7 but does not specifically teach wherein the neural network model is pre-learned with energy information of an electronic device including rated voltage, power consumption, and energy consumption efficiency as input values.
Ichikawa teaches wherein the neural network model is pre-learned with energy information of an electronic device including rated voltage, power consumption, and energy consumption efficiency as input values (e.g., Figure 5 shows a method for predicting the amount of electricity used dW. Here, we assume that this neural network has already been trained using past information (actual data and predictive data) as described above, and that the connection weight coefficients 57P and 58P within the neural network have been adjusted. [0030] Actual power information 10a such as the total amount of electricity used within the power receiving equipment and the amount of electricity used by major equipment, process parameter information 20a, and surrounding environment parameters 30 at time Tc are input into the neural network, and predictions are made for this information at time Tc+1, which is the next monitoring cycle. [0031] The neural network inputs the various pieces of information at the predicted time Tc+1 back into the neural network and predicts these pieces of information at the next monitoring cycle, Tc+2.) (Para. [0029]-[0031], also refer to Para. [0014]-[0015]).
23. Claim 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Hyun in view of Takahashi, and further in view of Ichikawa (JP2004297854A).
24. Regarding claim 11, the combination of Hyun and Takahashi teaches the artificial intelligence apparatus of claim 1 but does not specifically teach wherein the processor, when predicting the amount of power consumption in a home, inputs the first power consumption data obtained from a pre-registered electronic device and the second power consumption data estimated from a specific electronic device from which the first power consumption data has not been obtained into a pre-learned neural network model to predict the amount of power consumption in a home.
Ichikawa teaches wherein the processor, when predicting the amount of power consumption in a home, inputs the first power consumption data obtained from a pre-registered electronic device and the second power consumption data estimated from a specific electronic device from which the first power consumption data has not been obtained into a pre-learned neural network model to predict the amount of power consumption in a home (Refer to Para. [0012]-[0015], [0029]-[0031]).
Because Ichikawa is also directed to demand monitoring system, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Hyun, Takahashi, and Ichikawa before him/her, to modify the combined teachings of Hyun, and Takahashi to include the teaching of Ichikawa in order to improve the prediction accuracy of this predicted power consumption value, the prediction is performed by learning from actual data (Ichikawa: Para. [0016]).
25. Regarding claim 12, the combination of Hyun, Takahashi, and Ichikawa teaches artificial intelligence apparatus of claim 11, wherein Ichikawa further teaches the neural network model includes a time series forecasting model that predicts the t+1 value at time t (e.g., The basic principle of this patent is to predict the amount of electricity used dw within a certain unit of time from the present time Tc to Tc+1, add this to the actual demand Wr at time Tc to predict the demand at time Tc+1, further predict the amount of electricity used dw from time Tc+1 to time Tc+2, add this to the predicted demand value at time Tc+1 to obtain the predicted demand at time Tc+2) (Para. [0028]).
26. Regarding claim 13, the combination of Hyun, Takahashi, and Ichikawa teaches artificial intelligence apparatus of claim 11, wherein Ichikawa further teaches the processor, when predicting the amount of power consumption in a home, at least one of the user schedule data and the external environment data is additionally input to a pre-learned neural network model to predict the amount of power consumption in a home (e.g., On the other hand, the ambient environment parameter input unit 30 receives ambient environment state signals 4a from the ambient environment 4, such as outside temperature and humidity, and provides ambient environment parameter information 30a to the learning and prediction function unit 50 in real time. [0016] The learning prediction function unit 50 calculates the predicted power consumption value (dw) 50a for the next cycle and beyond at a predetermined monitoring cycle) (Para. [0015]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Youn (Pub: 2019/0011970) disclose a power demand prediction unit for using the
artificial intelligence engine to predict the power demand of the consumer within the specific period via at least one of a prediction algorithm, a statistical technique, a Delphi technique of an artificial intelligence based on the past power usage data, a weather forecast pattern, and a consumer characteristic pattern (Para. [0022]).
Tural (Pat:11710964) disclose the energy demand estimator 612 can be divided into two separate prediction models and as shown in FIG. 6B, an output of both models 624 and 628 are summed in block 630 for a final energy demand prediction, for example, over a subsequent 24 hours without any load shift or curtailing (Fig. 6B, Col. 16, Ln. 55-58, Col. 17, Ln. 6-9).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIGNESHKUMAR C PATEL whose telephone number is (571)270-0698. The examiner can normally be reached Monday - Friday, 7:00 AM - 5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth M. Lo can be reached at (571)272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JIGNESHKUMAR C PATEL/Primary Examiner, Art Unit 2116
Prosecution Insights