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
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d).
The certified copy has been filed in parent Application No. JAPAN 2020-126357, filed on 07/27/2020.
Status of Claims
Claims 1-9 are pending and examined herein.
Claims 1-9 are rejected under 35 U.S.C. 101.
Claims 1-9 are rejected under 35 U.S.C. 103.
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 - 9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP § 2109(III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject matter. The analysis of claims 1-20, in accordance with these steps, follows.
Step 1 Analysis:
Step 1 is to determine whether the claim is directed to a statutory category (process, machine, manufacture, or composition of matter.
Claims 1 - 7 are directed to an information processing apparatus, meaning that it is directed to the statutory category of machine. Claim 8 is directed to an information processing method, which is the statutory category of process. Claim 9 is directed to a non-transitory computer-readable storage medium storing a program, which can be an article of manufacture.
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
Regarding claim 1, the following claim elements are abstract ideas:
performing first clustering on the plurality of samples to generate a plurality of first clusters each including two or more samples, (Performing clustering is merely reciting mathematical calculation on the data, which is mathematical concept.)
classifying each of the plurality of first clusters as a second cluster satisfying a determination condition or a third cluster that does not satisfy the determination condition, the determination condition including at least one of a first criterion in which a variance of correlation values between the two or more samples is less than a first threshold and a second criterion in which an average of the correlation values exceeds a second threshold, (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
performing second clustering on the two or more samples included in the third cluster to divide the third cluster into a plurality of fourth clusters, (Performing clustering is merely reciting mathematical calculation on the data, which is mathematical concept.)
and generating training data, based on the second cluster and at least one of the plurality of fourth clusters, the training data being used for generating a model for predicting the power consumption. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
a memory that stores therein a plurality of samples each including time-series measurement values of power consumption; (This is mere transmitting data, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception and does not integrate the abstract idea into a practical applications.)
and a processor configured to perform a process including (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception and does not integrate the abstract idea into a practical applications.)
Regarding claim 2, the rejection of claim 1 is incorporated herein. Further, claim 2 recites the following additional element:
the determination condition includes both of the first criterion and the second criterion. (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception and does not integrate the abstract idea into a practical applications.)
Regarding claim 3, the rejection of claim 1 is incorporated herein. Further, claim 3 recites the following abstract idea:
calculating cross- correlations between the time-series measurement values for all pairs of samples as the correlation values and calculating at least one of the variance and the average of the cross-correlations. (Calculating correlations, variance, average is merely reciting mathematical calculations, which is mathematical concept.)
Claim 3 further recites following additional element:
the classifying includes, with respect to each of the plurality of first clusters, (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception and does not integrate the abstract idea into a practical applications.)
Regarding claim 4, the rejection of claim 1 is incorporated herein. Further, claim 4 recites the following abstract idea:
the generating of the training data includes generating the training data using the second cluster and one or more fourth clusters satisfying the determination condition among the plurality of fourth clusters. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 4 does not recite additional elements.
Regarding claim 5, the rejection of claim 1 is incorporated herein. Further, claim 5 recites the following abstract idea:
and generating the training data including the representative samples that are fewer than the plurality of samples. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 5 further recites following additional element:
the generating of the training data includes extracting representative samples from respective ones of the second cluster and the at least one of the fourth clusters (This is mere transmitting data, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception and does not integrate the abstract idea into a practical applications.)
Regarding claim 6, the rejection of claim 1 is incorporated herein. Further, claim 6 recites the following abstract idea:
each of the representative samples indicates an average of the time-series measurement values of samples included in a cluster from which the each of the representative samples is extracted. (Averaging the time-series measurement values is merely reciting mathematical calculation, which is mathematical concept.)
Claim 6 does not recite additional elements.
Regarding claim 7, the rejection of claim 1 is incorporated herein. Further, claim 7 recites the following abstract ideas:
the process further includes generating a neural network using measurement values taken during a first time period (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
and measurement values taken during a second time period following the first time period (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components.)
Claim 7 further recites following additional elements:
as input data, as teaching data, (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception and does not integrate the abstract idea into a practical applications.)
among the time-series measurement values of samples included in the training data, (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception and does not integrate the abstract idea into a practical applications.)
the neural network being used for predicting power consumption of the second time period from power consumption of the first time period. (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception and does not integrate the abstract idea into a practical applications.)
Regarding claim 8, the following claim elements are additional elements:
by a processor (These are mere instructions to apply abstract idea on a generic computer. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception and does not integrate the abstract idea into a practical applications.)
The rest of claim 8 and 9 recite substantially similar subject matter to claim 1 respectively and are rejected with the same rationale, mutatis mutandis.
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 (i.e., changing from AIA to pre-AIA ) 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, 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.
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.
Claims 1 – 2, 4 – 6, 8, 9 are rejected under 35 U.S.C. 103 as being unpatentable over Ball et al. (NPL: “ISODATA, A Novel Method of Data Analysis and Pattern Classification”) in view of Andrei et al. (U.S. Pub. 2018/0285788 A1).
Regarding Claim 1, Ball teaches
A processor configured to perform a process including performing first clustering on the plurality of samples to generate a plurality of first clusters each including two or more samples, (Pg. 12 B. Pictorial Flow Diagram section of Ball states “We show a pictorial flow diagram of ISODATA-POINTS in Fig. 1. In line with our considering ISODATA as a procedure for sorting patterns we show the patterns being fed into a sorter, one at a time, from a "pattern hopper." The patterns are sorted into subsets on the basis of distance from a set of cluster points -- each pattern going into that subset associated with the cluster point to which it, the pattern, is closest.” Pg. 13 Fig. 1 A Pictorial Description of ISODATA-Points of Ball shows the below system.
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Pg. 14 2nd Paragraph of Ball states “Those small clusters (with fewer than θn elements) are discarded at “valve 1”.”)
classifying each of the plurality of first clusters as a second cluster satisfying a determination condition or a third cluster that does not satisfy the determination condition, (Referring back to Fig. 1 and Pg. 14 3-4th paragraphs of Ball states “The criteria and method of splitting and lumping of clusters are given in detail in the next two sections. Splitting takes place if the standard deviation in any dimension is greater than θe and also if both (1) the cluster has enough members to split and (2) has high average distance between its mean and the patterns in its subset. Lumping occurs between, at most, the L pairs of means that are less than θc apart.” Those first clusters (the pills) are classified as either a second cluster satisfying a determination condition (splitting happens if the standard deviation of the cluster is greater than a threshold) so finding non-split clusters is classifying [some] of the plurality of first clusters as a second cluster satisfying a determination condition (of standard deviation being less than a threshold) and finding cluster that you will split is classifying [some] of the plurality of first clusters as a third cluster that does not satisfy the determination condition.)
the determination condition including at least one of a first criterion in which a variance of correlation values between the two or more samples is less than a first threshold and a second criterion in which an average of the correlation values exceeds a second threshold, (Referring back to Fig. 1 and Pg. 14 3-4th paragraphs of Ball states “The criteria and method of splitting and lumping of clusters are given in detail in the next two sections. Splitting takes place if the standard deviation in any dimension is greater than θe and also if both (1) the cluster has enough members to split and (2) has high average distance between its mean and the patterns in its subset. Lumping occurs between, at most, the L pairs of means that are less than θc apart.” Refer back to the previous limitation, these non-split clusters and non-lumping clusters would be where a variance is less than a first threshold and where average is higher than second threshold.)
performing second clustering on the two or more samples included in the third cluster to divide the third cluster into a plurality of fourth clusters, (Referring back to Fig. 1 and 5th paragraph of Ball states “After each lumping or splitting, the modified set of average points is used as the set of cluster points for the next iteration and placed in the "Sorter Box"”. After the samples from the split clusters are recirculated up and then reassigned to new cluster heads, we get performing second clustering on the two or more samples included in the third cluster to dived the third cluster into a plurality of fourth clusters (the third cluster was split, thus divided into a plurality of fourth clusters).)
, based on the second cluster and at least one of the plurality of fourth clusters, (Refer back to the recirculated fourth clusters and the second clusters where non-split clusters and non-lumping clusters were generated from the previous limitations. )
Ball does not explicitly teach
An information processing apparatus comprising: a memory that stores therein a plurality of samples each including time-series measurement values of power consumption;
and generating training data, … , the training data being used for generating a model for predicting the power consumption.
However, Andrei explicitly teaches that
An information processing apparatus comprising: a memory that stores therein a plurality of samples each including time-series measurement values of power consumption; ([0023] of Andrei states “receiving a plurality of energy consumption profiles, each profile comprising a time series of energy consumption data values;” and [0030] of Andrei states “The invention also provides a system or apparatus having means, preferably in the form of a processor and associated memory, for performing a method as set out in any of above aspects (or as described elsewhere herein).”)
and generating training data, … , the training data being used for generating a model for predicting the power consumption. (Fig. 1 of Andrei shows process of clustering (identify closest N neighbors) and using it to derive predictive model then determine and output predicted consumption
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value. )
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Ball and Andrei because Ball teaches iterative clustering with splitting and lumping based on determination condition with threshold and Andrei teaches generating predictive models for power consumption using clustered dataset as training data for predictive model. One with ordinary skill in the art would be motivated to incorporate the clustering techniques of Ball into the predictive modeling of Andrei because using well-refined data by multistage clustering would predictably improve the accuracy of the generated predictive model. Therefore, it would have been obvious to combine the iterative clustering method from Ball with the system to generate predictive model for power consumption prediction in Andrei to merely apply known clustering refinement methods to the known use of clustered data in predictive modeling, yielding a predictive improvement in training data quality.
Regarding Claim 2, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Ball and Andrei teaches
the determination condition includes both of the first criterion and the second criterion. (Pg. 14 3rd paragraph of Ball states “Splitting takes place if the standard deviation in any dimension is greater than θe and also if both (1) the cluster has enough members to split and (2) has high average distance between its mean and the patterns in its subset”)
Regarding Claim 4, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Ball and Andrei teaches
generating of the training data includes generating the training data using the second cluster and one or more fourth clusters satisfying the determination condition among the plurality of fourth clusters. (Referring back to Fig. 1 and Pg. 14 3-5th paragraphs of Ball from previous limitations, , non-split/non-lumped clusters represent the second cluster where the determination condition is met. As the clusters recirculate to be clustered furthermore, it will go through the same classifying step (splitting/lumping based on condition) and you can use the data from second clusters with fourth clusters that satisfy the conditions through classifying step)
Regarding Claim 5, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Ball and Andrei teaches
generating of the training data includes extracting representative samples from respective ones of the second cluster and the at least one of the fourth clusters and generating the training data including the representative samples that are fewer than the plurality of samples. (Referring back to Fig. 1 and Pg. 14 3-5th paragraphs of Ball states “The criteria and method of splitting and lumping of clusters are given in detail in the next two sections. Splitting takes place if the standard deviation in any dimension is greater than θe and also if both (1) the cluster has enough members to split and (2) has high average distance between its mean and the patterns in its subset. Lumping occurs between, at most, the L pairs of means that are less than θc apart. After each lumping or splitting, the modified set of average points is used as the set of cluster points for the next iteration and placed in the "Sorter Box" ” for cluster splitting mechanism and [0023] of Andrei states “The method may then further comprise outputting data representative of the determined clustering, for example by outputting data defining assignments of profiles to clusters, outputting graphical representations of cluster assignments, outputting representative time series (e.g. medoid time series or averaged time series) for identified clusters (optionally as graphical time series representations), and the like.” [0137] of Andrei states “For example, multiple time series of half-hourly data each covering a respective day may be averaged (or otherwise combined) to produce a single representative time series for that household.”)
Regarding Claim 6, the rejection of claim 5 is incorporated herein. Furthermore, the combination of Ball and Andrei teaches
each of the representative samples indicates an average of the time-series measurement values of samples included in a cluster from which the each of the representative samples is extracted. ([0023] of Andrei states “The method may then further comprise outputting data representative of the determined clustering, for example by outputting data defining assignments of profiles to clusters, outputting graphical representations of cluster assignments, outputting representative time series (e.g. medoid time series or averaged time series) for identified clusters (optionally as graphical time series representations), and the like.”)
Claims 8, 9 recite substantially similar subject matter as claim 1 respectively, and are rejected with the same rationale, mutatis mutandis.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Ball et al. (NPL: “ISODATA, A Novel Method of Data Analysis and Pattern Classification”) in view of Andrei et al. (U.S. Pub. 2018/0285788 A1), further in view of Li et al. (NPL: “A novel clustering algorithm for time-series data based on precise correlation coefficient matching in the IoT”).
Regarding Claim 3, the rejection of claim 1 is incorporated herein. The combination of Ball and Andrei does not explicitly teach
classifying includes, with respect to each of the plurality of first clusters, calculating cross- correlations between the time-series measurement values for all pairs of samples as the correlation values and calculating at least one of the variance and the average of the cross-correlations.
However, Li teaches
classifying includes, with respect to each of the plurality of first clusters, calculating cross- correlations between the time-series measurement values for all pairs of samples as the correlation values and calculating at least one of the variance and the average of the cross-correlations. (Pg. 6654 Abstract of Li states “To analyze the correlation, a clustering algorithm named the CPCCM (clustering algorithm based on precise correlation coefficient matching) is presented. First, each initial sequence is split into a set of subsequences by adopting a preset sliding window. Then, the correlation coefficients between any pair of subsequence sets from two sequences are resolved. Those pairs that pass some preset Pearson correlation coefficient threshold are clustered.”)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Ball, Andrei and Li because Li teaches clustering electric power consumption timeseries data using Pearson correlation thresholds. One with ordinary skill in the art would be motivated to incorporate the teachings of Li into the combination of Ball and Andrei because Li provides a way to ensure that clusters reflect meaningful temporal patterns in electrical power consumption data, while Andrei shows how these clustered data can be used to build predictive models for prediction of power consumption data. Therefore, the combination of Ball, Andrei with Li would have been obvious for a person of ordinary skill in the art.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Ball et al. (NPL: “ISODATA, A Novel Method of Data Analysis and Pattern Classification”) in view of Andrei et al. (U.S. Pub. 2018/0285788 A1), further in view of Anupiya et al. (NPL: “Predicting Electricity Consumption using Deep Recurrent Neural Networks”).
Regarding Claim 7, the rejection of claim 1 is incorporated herein. The combination of Ball and Andrei does not explicitly teach
the process further includes generating a neural network using, as input data, measurement values taken during a first time period and, as teaching data, measurement values taken during a second time period following the first time period, among the time-series measurement values of samples included in the training data,
the neural network being used for predicting power consumption of the second time period from power consumption of the first time period.
However, Anupiya teaches
the process further includes generating a neural network using, as input data, measurement values taken during a first time period and, as teaching data, measurement values taken during a second time period following the first time period, among the time-series measurement values of samples included in the training data, (Pg. 4 section 3.2 Setting up training and testing data of Anupiya states “The dataset is divided into training and testing data. The testing data is kept separate from the training data. Therefore, the testing data is unseen to the model until testing the models. Given three months and predicting the next three months. Balance dataset is achieved by using 600 days. Three months were given to predict three months ahead”)
the neural network being used for predicting power consumption of the second time period from power consumption of the first time period. (Pg. 4 section 3.3 RNN of Anupiya states “RNN is deep learning model which learn from sequences [6]. RNN recursively takes the past output (𝑦𝑡−1) and adds it to the current input (𝑥𝑡). RNN current output (𝑦𝑡) learns from the past sequence, using the 𝑦𝑡−1. Fig.1 shows one RNN unit structure. The sequence is passed on as an input to the current input. Therefore, 𝑦𝑡 would be influenced by the past sequences. The past sequences carries on the past results with a combination”)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify combination of Ball and Andrei in view of Anupiya because Anupiya teaches training recurrent neural networks or long short-term memory network to predict future power consumption based on preceding time-series data of consumption. One with ordinary skill in the art would be motivated to incorporate the teachings of Anupiya into the combination of Ball and Andrei because using past consumption values to predict future values is a well-known approach for predictive models, and clustering timeseries data provides more representative training set for neural network. Therefore, it would have been obvious to combine the teachings of Ball, Andrei with Anupiya to predict power consumption using neural network.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BYUNGKWON HAN whose telephone number is (571)272-5294. The examiner can normally be reached M-F: 8:30AM-6PM PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen can be reached at (571)272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BYUNGKWON HAN/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121