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 Arguments
Applicant’s arguments with respect to claim(s) are directed to new amendments which is addressed in the new ground of rejection 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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to and abstract idea without significantly more.
Step 1:
Claims 1-8 are directed to a method Therefore, each of the claims is directed to one of the four statutory categories of patent eligible subject matter.
Step 2A Prong 1:
Claim 1 recites:
“clustering working states of electrical appliances through a clustering algorithm, calculating average values and standard deviation of each cluster, and encoding the working states of electrical appliances” this is a mathematical process
“establishing a hidden Markov model with multiple parameters and calculating model parameters on the training data, wherein the model parameters comprise a state transfer matrix, an output matrix, and an initial probability matrix;” this is a mathematical process
“performing state recognition based on Viterbi algorithm using the trained hidden Markov model and obtaining a predicted state sequence;” this is a mathematical process
“according to the predicted state sequence and statistical values of each cluster, decomposing a load power based on maximum likelihood estimation principle” this is a mathematical process
“performing clustering on the test data using a clustering algorithm” this is a mathematical process
“selecting active power and steady-state current data of each sampling point of each electrical appliance from the data set” this is a mental process
“clustering the working states of the electrical appliances through the clustering algorithm, calculating the average values and the standard deviation of each cluster, and encoding the working states of the electrical appliances comprises: clustering the working states of electrical appliances by using k-means clustering algorithm, and calculating the average values and standard of each cluster after the clustering results were obtained” this is a mathematical process
“performing state coding to each electrical appliance, so as to encode working state vector of each electrical appliance into a binary state; wherein the method of performing the state coding to each electrical appliance, so as to encode the working state vector of each electrical appliance into the binary state comprises: step 2.1, allocating bits, comprising: determine binary bits required for encoding according to the number of states of electrical appliances; step 2.2, determining values, comprising: calculating binary state values according to decimal state values of the electrical appliances at current moment; and step 2.3, splicing representation, comprising: splicing, according to the order of electrical appliances, the binary state values from high to low to get a final result.” this is a mathematical process
Step 2A Prong 2:
The additional elements:
“obtaining power fingerprint of each electrical appliance to generate training data and test data, importing the test data, outputting state sequence and power decomposition result wherein the method of the step l's obtaining the power fingerprint of each electrical appliance to generate the training data and the test data comprises: obtaining the power fingerprint of each electrical appliance; wherein the power fingerprint of each electrical appliance comprises the active power and the steady-state current data collected over at least 14,400 sampling points during a continuous 10-day period; and dividing the selected active powers and the steady-state current data into groups according to time as the training data and the test data such that the selected active powers and the steady-state current data are grouped into multiple sets by time sequence to form a training dataset and a test dataset, wherein the power fingerprint of each electrical appliance includes the active power and the history data of 1st to 11th harmonics of steady-state operating current of each electrical appliance;” are all directed to insignificant extra solution activity (see MPEP 2106.05(g)
Step 2B: The elements judged as insignificant extra-solution activity, “obtaining power fingerprint of each electrical appliance to generate training data and test data, importing the test data, outputting state sequence and power decomposition result wherein the method of the step l's obtaining the power fingerprint of each electrical appliance to generate the training data and the test data comprises: obtaining the power fingerprint of each electrical appliance; wherein the power fingerprint of each electrical appliance comprises the active power and the steady-state current data collected over at least 14,400 sampling points during a continuous 10-day period; and dividing the selected active powers and the steady-state current data into groups according to time as the training data and the test data such that the selected active powers and the steady-state current data are grouped into multiple sets by time sequence to form a training dataset and a test dataset, wherein the power fingerprint of each electrical appliance includes the active power and the history data of 1st to 11th harmonics of steady-state operating current of each electrical appliance” amounts to mere data gathering and outputting (see MPEP 2106.05(g)
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as seen from above, the respective claim elements taken individually do not amount to significantly more than the judicial exception. When taken as a whole (in combination), the claim also does not amount to significantly more than the abstract idea because the additional elements generically describe some computer hardware elements for performing the abstract idea and add no meaningful limitation beyond that of the abstract idea.
Claim 2 recites:
Step 2A Prong 1:
“selecting active power and steady-state current data of each sampling point of each electrical appliance from the data set; dividing the selected active powers and steady-state current data into groups according to time as the training data and the test data, wherein the power fingerprint of each electrical appliance includes the active power and the history data of 1st to 11t' harmonics of steady-state operating current of each electrical appliance.” are seen as mental process
Step 2A Prong 2:
The additional elements:
“obtaining the power fingerprint of each electrical appliance” is directed to insignificant extra solution activity (see MPEP 2106.05(g)
Step 2B: The elements judged as insignificant extra-solution activity, “obtaining the power fingerprint of each electrical appliance” amounts to mere data gathering and outputting (see MPEP 2106.05(g)
Claim 3 recites:
Step 2A Prong 1:
“clustering the working states of electrical appliances by using k- means clustering algorithm, and calculating the average values and standard of each cluster after the clustering results were obtained and performing state coding to each electrical appliance, so as to encode working state vector of each electrical appliance into a binary state.” is a mathematical process
Claim 4 recites:
Step 2A Prong 1:
“allocating bits, comprising: determine binary bits required for encoding according to the number of states of electrical appliances; splicing representation, comprising: splicing, according to the order of electrical appliances, the binary state values from high to low to get a final result” is a mental process
“determining values, comprising: calculating binary state values according to decimal state values of the electrical appliances at current moment” is a mathematical process
Claim 5 recites:
Step 2A Prong 1:
“using S to represent a set of combined operating states of each electrical appliance, and that S is a set of total states, wherein the set a complete sorting of the operating states of each electrical appliance, and the number of elements in the set is determined by the number of clusters of the states of each electrical appliance; step 3.2, using V to represent total power fingerprint set of total user power consumption, elements of set V, represented as vi= [P/',I[], include vectors constructed by total active power and total steady-state current;” can be done by a human with a pen and paper is a mental process.
“establishing a state transfer matrix A, comprising aij indicates a probability of each electrical appliance's transferring from total states qt=si at time t transferred to total states qt+1=sj at time t+1, where the calculation is:
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Where hij is frequency of the transferring from the total states qt=si at time t to the total states qt+1=sj at time t+1, N is total number of implicit states; step 3.4, establishing an output matrix B, comprising bik indicates a probability that each electrical appliance is under the total states qt=si at time t and observation value is yt=vk, where the calculation is:
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where oik is frequency of each electrical appliance is under the total states qt=si at time t and the observation value is yt=vk, and M is the total number of the observation value; and step 3.5, initial probability matrix, comprising: riT indicates a probability that each electrical appliance is under si at an initial time, where the calculation is:
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where d is the total number of training data set, and dI indicates frequency of the implicit stat si existed in the training data set.
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” Is a mathematical process
Claim 6 recites:
Step 2A Prong 1:
“wherein method of the step 5's performing the state recognition based on the Viterbi algorithm and obtaining the predicted state sequence comprises: step 5.1, initialization:
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step 5.2, recursive calculation:
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step 5.3, termination state calculation:
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step 5.4, optimal sequence backtracking: where, obtained sequence is the predicted optimal implicit state
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” is a mathematical process.
Claim 7 recites:
Step 2A Prong 1:
“step 6.1, according to the average value and variance of the cluster of each electrical appliance sample, establishing a normal distribution probability density function of each electrical appliance in each state; and step 6.2, establishing an objective function based on maximum likelihood estimation, so as to find the maximum of joint probability.” Is a mathematical process
Claim 8 recites:
Step 2A Prong 1:
“the objective function is:
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where, o-pj and y[i,] respectively indicates the standard deviation and the average value of Phcluster of the ith electrical appliance, N is the number of electrical appliances, P(i) indicates decomposed active power of each electrical appliance, and PLindicates the active power of the total loading, f[i,](P(')) indicates probability of ith electric appliance which is in jth operating state to consume power P(.” is a mathematical process
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 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 ANN J LO whose telephone number is (571)272-9767. The examiner can normally be reached Monday-Friday, 9 AM to 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Cordelia Zecher can be reached at (571)272-7771. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANN J LO/Supervisory Patent Examiner, Art Unit 2159