DETAILED ACTION
This communication is in response to the application filed 11/22/23 in which claims 1-14 were presented for examination.
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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6 and 13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 6 and 13 recite that the second-type model is different from the first-type model and that the first-type model is a neural network deep learning model and the second-type model is a machine learning model. However, neural network models are a type of machine learning model. Thus, the first-type and second-type models are not different from each other, which contradicts the first limitation describing the models as different.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1/8
An electrical appliance status analysis device, electrically connected to an electricity meter, comprising:
a storage unit, configured to store a plurality of user information feature data, a plurality of electricity meter sequence data, and a plurality of appliance status sequence data, wherein each appliance status sequence data corresponds to a respective appliance type, and has the same time series as the corresponding electricity meter sequence data;
a processor, electrically connected to the storage unit, configured to read the plurality of user information feature data, the plurality of electricity meter sequence data, and the plurality of appliance status sequence data, and performing the following steps to the appliance status sequence data corresponding to the same appliance type:
defining said appliance type as a high-correlation appliance or a low-correlation appliance according to an average correlation coefficient of the plurality of appliance status sequence data and the corresponding electricity meter sequence data;
inputting the plurality of appliance status sequence data defined as high-correlation appliance and the corresponding electricity meter sequence data into a first-type model to perform model training, and generating a first-type appliance status analysis model for said appliance type;
performing a feature extraction process on the plurality of appliance status sequence data defined as low-correlation appliance and corresponding electricity meter sequence data, generating a plurality of appliance feature data and a plurality of electricity meter feature data, and inputting the plurality of appliance feature data, the plurality of electricity meter feature data and the plurality of user information feature data into a second-type model to perform model training, and generating a second-type appliance status analysis model for said appliance type; and
inputting an unanalyzed electricity meter sequence data into the first-type appliance status analysis model, or inputting an unanalyzed electricity meter feature data and an unanalyzed user information feature data into the second-type appliance status analysis model, and generating at least one appliance status analysis result corresponding to at least one of the appliance types.
Step 1: YES. Claim 1 is directed to a system and, therefore, falls under a statutory category.
Step 2A Prong 1: YES. Claim 1 recites “defining said appliance type as a high-correlation appliance or a low-correlation appliance according to an average correlation coefficient of the plurality of appliance status sequence data and the corresponding electricity meter sequence data” and “performing a feature extraction process on the plurality of appliance status sequence data defined as low-correlation appliance and corresponding electricity meter sequence data, generating a plurality of appliance feature data and a plurality of electricity meter feature data,” which encompasses a mental process capable of being performed by a user by evaluation, opinion, and judgment. Accordingly, claim 1 recites an abstract idea.
Step 2A Prong 2/Step 2B: NO. The additional elements fail to integrate the judicial exception into a practical application or provide an inventive concept.
The steps of inputting the appliance status sequence data defined as high-correlation appliance and the corresponding electricity meter sequence data into a first type model to perform model training, are data inputting steps and constitute insignificant extra-solution activity. Generating a first type appliance model is describing a data outputting step and is also insignificant extra-solution activity. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “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.” The sub-limitation of performing model training is recited at a high level of generality and, therefore, constitutes mere instruction to apply the exception.
Similarly, the limitation of inputting the appliance feature data, the electricity meter feature data and the user information feature data into a second model is data inputting and the generating of a second-type appliance status analysis model is data outputting and are considered insignificant extra-solution activities. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “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.” The sub-limitation of performing model training is recited at a high level of generality and, therefore, constitutes mere instruction to apply the exception.
The limitation reciting inputting unanalyzed meter data into the first model or inputting electricity meter feature data and user information feature data into the second model, and generating an appliance status result are data inputting and outputting, considered insignificant extra-solution activity. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “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.”
The additional element of a storage unit storing various data recites a generic computer component at a high level of generality and is considered mere instruction to apply the exception. Similarly, the additional element of a processor connected to the storage unit is mere instruction to apply the exception.
Claim 8 is a method claim corresponding to claim 1 and, therefore, is similarly analyzed.
Accordingly, claims 1 and 8 are ineligible.
Claim 2/9
The analysis device as claimed in claim 1, wherein plurality of data points in the appliance status sequence data each correspond to one of an on-state tag and an off-state tag.
Step 2A Prong 2/Step 2B: NO. The type of input data does not cause the data inputting to integrate the judicial exception into a practical application. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “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.”
Claim 9 is a method claim corresponding to claim 2 and, therefore, is similarly analyzed.
Accordingly, claims 2 and 8 are ineligible.
Claim 3/10
The analysis device as claimed in claim 2, wherein the processor is further configured to execute a status tagging procedure, which includes dividing the time series of an appliance electricity usage original data by a tagging cycle, comparing a value of a plurality of data points in each tagging cycle to an activated load threshold value, and calculating a number of the data points in each tagging cycle that exceed the activated load threshold value;
if the number of data points that exceed the activated load threshold value within the tagging cycle is higher than a comparing number threshold value, determining the plurality of data points in the tagging cycle correspond to the on-state tag;
if the number of data points that exceed the activated load threshold value within the tagging cycle is lower than the comparing number threshold value, determining the plurality of data points in the tagging cycle correspond to the off-state tag.
Step 2A Prong 1: YES. The claim recites dividing the time series according to a tagging cycle, comparing the data points in each cycle to an activated load threshold. The claim further recites comparing the number of data points that exceed the activated load threshold value within the tagging cycle to a threshold value to determine whether the data points correspond to an on-state or off-state tag. These steps may be performed by evaluation, judgment, or opinion by a user and, therefore, fall under the Mental Processes grouping of abstract ideas. The claim also recites calculating a number of data points in each tagging cycle that exceed the activated threshold value. Calculations, whether performed mentally or with the aid of pen and paper or computer, fall under the Mathematical Concepts grouping of abstract ideas.
Claim 10 is a method claim corresponding to claim 3 and, therefore, is similarly analyzed.
Accordingly, claims 3 and 10 are ineligible.
Claim 4/11
The analysis device as claimed in claim 3, wherein a data frequency of the plurality of electricity meter sequence data and the plurality of appliance electricity usage original data is below 1 Hertz.
Step 2A Prong 2/Step 2B: NO. Claim 3 further describes the electricity meter sequence data and appliance electricity usage original data as having a frequency below 1 Hz. The type of input data does not cause the data inputting step to integrate the exception into a practical application. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “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.”
Claim 11 is a method claim corresponding to claim 4 and, therefore, is similarly analyzed.
Accordingly, claims 4 and 11 are ineligible.
Claim 5/12
The analysis device as claimed in claim 1, wherein defining said appliance type as a high-correlation appliance or a low-correlation appliance according to an average correlation coefficient of the appliance status sequence data and the corresponding electricity meter sequence data further comprising:
calculating a Pearson correlation coefficient of each appliance status sequence data and the corresponding electricity meter sequence data, and calculating the average correlation coefficient of the Pearson correlation coefficients of the plurality of appliance status sequence data;
comparing the average correlation coefficient with a correlation threshold value;
if the average correlation coefficient is higher than the correlation threshold value, defining the appliance type corresponding to the plurality of appliance status sequences is defined as a high-correlation appliance;
if the average correlation coefficient is lower than the correlation threshold value, defining the appliance type corresponding to the plurality of appliance status sequences is defined as a low-correlation appliance.
Step 2A Prong 1: YES. Calculating a Pearson correlation coefficient involves a mathematical calculation and, therefore, falls under the Mathematical Concepts grouping of abstract ideas. Comparing the average correlation coefficient with a threshold value and defining the appliance type as a high-correlation or low-correlation appliance can be performed by evaluation, opinion, and judgment and, therefore, falls under the Mental Processes grouping of abstract ideas.
Claim 12 is a method claim corresponding to claim 5 and, therefore, is similarly analyzed.
Accordingly, claims 5 and 12 are ineligible.
Claim 6/13
The analysis device as claimed in claim 1, wherein the second-type model is different from the first-type model; the first-type model is a neural network deep learning model, and the second-type model is a machine learning model.
Step 2A Prong 2/Step 2B: NO. The additional elements of the claim further describe the first-type and second-type models as being either a neural network model or a machine learning model. Recitation of generic computer components recited at a high level of generality is considered mere instruction to apply the exception.
Claim 13 is a method claim corresponding to claim 6 and, therefore, is similarly analyzed.
Accordingly, claims 6 and 13 are ineligible.
Claim 7/14
The analysis device as claimed in claim 1, wherein the user information feature data includes at least one of the following or a combination thereof: user routine survey information, user appliance usage habit survey information, user household member survey information, and user electricity meter load classification label information.
Step 2A Prong 2/Step 2B: NO. Claim 7 further describes the user information feature data. The type of input data does not cause the data inputting step to integrate the exception into a practical application. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “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.”
Claim 14 is a method claim corresponding to claim 7 and, therefore, is similarly analyzed.
Accordingly, claims 7 and 14 are ineligible.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zhou, Yongjun, et al. "Event-based two-stage non-intrusive load monitoring method involving multi-dimensional features." CSEE Journal of Power and Energy Systems 9.3 (2022): 1119-1128.
Klemenjak, Christoph, and Wilfried Elmenreich. "On the applicability of correlation filters for appliance detection in smart meter readings." 2017 IEEE International Conference on Smart Grid Communications (SmartGridComm). IEEE, 2017.
Lin, Yu-Hsiu. "Design and implementation of an IoT-oriented energy management system based on non-intrusive and self-organizing neuro-fuzzy classification as an electrical energy audit in smart homes." applied sciences 8.12 (2018): 2337.
Massidda, Luca, Marino Marrocu, and Simone Manca. "Non-intrusive load disaggregation by convolutional neural network and multilabel classification." Applied Sciences 10.4 (2020): 1454.
Zhou, Xiao, et al. "Non-intrusive load monitoring using a CNN-LSTM-RF model considering label correlation and class-imbalance." IEEE Access 9 (2021): 84306-84315.
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHID KHAN whose telephone number is (571)270-0419. The examiner can normally be reached M-F, 9-5 est.
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/SHAHID K KHAN/ Primary Examiner, Art Unit 2146
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