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 .
Priority
Application 17/779,187 filed on 07/18/2023 is a 371 of PCT/CN2021/105242 filed on 07/08/2021 and claims foreign priority to CHINA 202010877531.4 filed on 08/27/2020.
Current Status
This office action is a first office action, non-final rejection based on the merits where in claims 1-9 are pending and have been considered below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite an abstract idea as discussed below. This abstract idea is not integrated into a practical application for the reasons discussed below. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons discussed below.
Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, the claims belong to one of the statutory classes of a process or product as a computer implemented method or a computer system/product.
Step 2A of the 2019 Guidance is divided into two Prongs. Prong 1 requires the examiner to determine if the claims recite an abstract idea, and further requires that the abstract idea belong to one of three enumerated groupings: mathematical concepts, mental processes, and certain methods of organizing human activity.
Claim 1 is copied below, with the limitations belonging to an abstract idea being underlined.
A non-intrusive load monitoring method, comprising a device classification and prediction sub- process, a new device identification sub-process, and
a classifier self-training sub-process;
the new device identification sub-process comprises following steps:
step 1. making large dynamic labeling on results of transient events for detecting whether a periodic power transition device exists;
step 2. determining whether there is periodic power transition by using a peak filter method, so as to label whether the detected events have periodic changes and separate aperiodic large dynamic events from the detected events;
step 3. correcting event detection results and correcting data of stable segment to obtain possible periodic power transition device from the corrected data, and correcting prediction of electrical device again;
step 4. intercepting waveform data of stable operation segment of the electrical device, inputting the intercepted waveform data as information, calculating a number of the stable operation segments of the electrical device in a predetermined time according to recorded start time point and end time point of each transient event; wherein no electrical device is restarted between the start time point and the end time point; and labeling and recording a segment number corresponding to the start time point and the end time point;
step 5. performing feature extraction on the waveform data of the stable operation segment of each electrical device;
step 6. identifying whether an unknown device is a new device by using feature similarity discrimination index;
step 7. subtracting the waveform data of previous stable operation segment of the electrical devices from the waveform data of the stable operation segment of electrical devices having the new devices so as to separate out waveform data of the new device.
Regarding: “step 1. making large dynamic labeling on results of transient events for detecting whether a periodic power transition device exists” the underlined limitation is an abstract idea as it is a set of programming routines and patterns for automatic labeling based on changing data therefore it is an algorithm or program which is a mathematical routine.
Regarding: “step 2. determining whether there is periodic power transition by using a peak filter method, so as to label whether the detected events have periodic changes and separate aperiodic large dynamic events from the detected events” the underlined limitation is an abstract idea as it is a set of programming routines and patterns for detecting and separating events using a peak filter which is a frequency selective filter therefore it is an algorithm or program which is a mathematical routine.
Regarding: “step 3. correcting event detection results and correcting data of stable segment to obtain possible periodic power transition device from the corrected data, and correcting prediction of electrical device again” the underlined limitation is an abstract idea as, using the broadest reasonable interpretation, it is a set of programming routines and patterns for correcting data and then identifying and correcting the prediction of the electrical device again therefor it is an algorithm or program which is a mathematical routine.
Regarding: “step 4. intercepting waveform data of stable operation segment of the electrical device, inputting the intercepted waveform data as information, calculating a number of the stable operation segments of the electrical device in a predetermined time according to recorded start time point and end time point of each transient event; wherein no electrical device is restarted between the start time point and the end time point; and labeling and recording a segment number corresponding to the start time point and the end time point” the underlined limitations are abstract ideas as, using the broadest reasonable interpretation, it is a set of programming routines and patterns for intercepting data, using the data to calculate the number of stable operation segments in a specific time frame, and labeling the segment number with respect to the start time and end time therefore it is an algorithm or program which is a mathematical routine.
Regarding: “step 5. performing feature extraction on the waveform data of the stable operation segment of each electrical device” the underlined limitation is an abstract idea as it is a set of programming routines and patterns for transforming raw data using feature extraction therefore it is an algorithm or program which is a mathematical routine.
Regarding: “step 6. identifying whether an unknown device is a new device by using feature similarity discrimination index” the underlined limitation is an abstract idea as a feature similarity discrimination index is a set of programming routines and patterns therefore it is an algorithm or program which is a mathematical routine.
Regarding “step 7. subtracting the waveform data of previous stable operation segment of the electrical devices from the waveform data of the stable operation segment of electrical devices having the new devices so as to separate out waveform data of the new device” the underlined limitation is an abstract idea as, using the broadest reasonable interpretation, subtracting is a set of programming routines and patterns therefore it is an algorithm or program which is a mathematical routine.
In summary, the underlined steps in the claim above therefore recite an abstract idea at Prong 1 of the 101 analysis.
The additional elements in the claim have been left in normal font.
This judicial exception is not integrated into a practical application because the additional elements within the claim only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)]. The claim recites “inputting the intercepted waveform data as information.” Examiner could find no explanation of how data is input in the specification, therefore using the broadest reasonable interpretation “inputting” data is done either by human activity or using a computer.
The additional limitations in relation to the computer, computer product, or computer system does not offer a meaningful limitation beyond generally linking the use of the method to a computer (see Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 217, 110 USPQ2d 1976, 1981 (2014)). The claim does not recite a particular machine applying or being used by the abstract idea.
The claims do not integrate the abstract idea into a practical application. Various considerations are used to determine whether the additional elements are sufficient to integrate the abstract idea into a practical application. The claim does not recite a particular machine applying or being used by the abstract idea. The claim does not effect a real-world transformation or reduction of any particular article to a different state or thing. (Manipulating data from one form to another or obtaining a mathematical answer using input data does not qualify as a transformation in the sense of Prong 2.)
The claim does not contain additional elements which describe the functioning of a computer, or which describe a particular technology or technical field, being improved by the use of the abstract idea. (This is understood in the sense of the claimed invention from Diamond v Diehr, in which the claim as a whole recited a complete rubber-curing process including a rubber-molding press, a timer, a temperature sensor adjacent the mold cavity, and the steps of closing and opening the press, in which the recited use of a mathematical calculation served to improve that particular technology by providing a better estimate of the time when curing was complete. Here, the claim does not recite carrying out any comparable particular technological process.) In all of these respects, the claim fails to recite additional elements which might possibly integrate the claim into a particular practical application. Instead, based on the above considerations, the claim would tend to monopolize the abstract idea itself, rather than integrate the abstract idea into a practical application.
Step 2b of the 2019 Guidance requires the examiner to determine whether the additional elements cause the claim to amount to significantly more than the abstract idea itself. The considerations for this particular claim are essentially the same as the considerations for Prong 2 of Step 2a, and the same analysis leads to the conclusion that the claim does not amount to significantly more than the abstract idea.
Therefore, claim 1 is rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more.
Dependent claims 2-10 are similarly ineligible.
Claim 2 merely adds limitations which further detail and limit the abstract idea, namely further mathematical steps detailing how the data processing algorithm is implemented, i.e. additional software limitations including “preprocessing current and voltage data of the electrical devices,” “the preprocessing includes removing outliers and interpolating,” “detecting event behaviors, including detecting occurrence of events and distinguishing transient event and steady state event; wherein when the event detected is classified into the transient event,” “performing feature extraction based on the waveform data of the stable operation segment, and extracting operating state features of the electrical device,” “invoking a classifier model for prediction,” “classifier model parameters generated by training are invoked for prediction,” “analyzing classification results of the classifier model to obtain energy-using information of the electrical device,”
“the classifier self-training sub-process is performed after the waveform data of the new devices is separated out in step 7 of the new devices identification sub-process,” “generating waveform data of comprehensive state: generating a variety of permutations and combinations of the device number composed of different numbers by invoking the device numbers in the device database and using a calculation method of permutation and combination of the device numbers; superposing the corresponding waveform data of the devices based on the permutations and combinations of different numbers according to the obtained permutations and combinations and the waveform data of the steady state of each device in the device database, so as to obtain multiple segments of waveform data of comprehensive state superposed by different waveform data of the devices,” “performing feature extraction on current waveform data during combined operation of multiple electrical devices to obtain feature data set, and dividing the obtained feature data set into a training set and a test set, and then performing parameter training by using a machine learning classifier model, as well as accurately predicting behaviors of the electrical device,” “evaluating model results: statistically analyzing the prediction results of each electrical device of each cycle in n consecutive cycles within each intercepted time period of the waveform data of the stable segment of the devices, so as to determine the condition ratio of starting and stopping of the devices,” and “feature similarity is compared according to step 6 of the new device identification sub-process; if there is a new device, the classifier self- training sub-process is performed, and step 5 of the device classification and prediction sub-process is then performed after the model training is completed; if there is no new device, the step 5 of the device classification and prediction sub-process is directly performed to analyze which device is operating and when to start and stop it, so as to obtain the energy-using information of electrical devices. Each of these limitations are sets of programming routines and patterns therefore are algorithms or programs which are mathematical routines.
Claims 3-8 and 10 each recite mathematical equations including the well known Discrete Fourier Transform (DFT), Inverse Discrete Fourier Transform (IDFT), and Discrete time Fourier Transform (DtFT). Under the broadest reasonable interpretation of the limitations, these limitations are best characterized as representing mathematical relationships - see MPEP § 2106.04(a)(2)(I)(A).
Claim 9 recites well-known classifiers models such as a neural network, K-means clustering, support vector machine, or random forest.
Considering all the limitations individually and in combination, the claimed additional elements do not show any inventive concept to applying algorithms such as improving the performance of a computer or any technology, and do not meaningfully limit the performance of the application.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Denise R Karavias whose telephone number is (469)295-9152. The examiner can normally be reached 7:00 - 3:00 M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Arleen M. Vazquez can be reached at 571-272-2619. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DENISE R KARAVIAS/Examiner, Art Unit 2857
/ARLEEN M VAZQUEZ/Supervisory Patent Examiner, Art Unit 2857
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