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 .
DETAILED ACTION
This action is response to the communication filed on November 10, 2025. Claims 1, 4-9 are pending. Claims 2-3 are canceled by applicant.
Response to Arguments
Applicant's arguments filed on November 10, 2025 have been fully considered but they are not persuasive.
Response to 101 rejection:
Regarding 101 rejection applicant argues, the recited power system and server do not involve the use of a general purpose computer and it represents a technological innovation deeply integrated into practical applications.
In response examiner respectfully disagree. The power system and server as recited in the claim is nothing but a generic computer component. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. With respect to integration of the abstract idea into a practical application, the additional element of clustering, averaging, and segmenting steps amounts to no more than mere instructions to apply the exception using a generic computer component. In the view applicant specification, it appears that the claim steps are done using mathematical calculation with the combination of generic computer component, which fall under mental process and/or mathematical concept. Note that the courts have recognized these functions as well‐understood, routine, and conventional as they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II, 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 (utilizing an intermediary computer to forward information)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Response to 103 rejection:
(1) Applicant argues Yang fails to disclose segmenting and averaging the to-be-classified load data according to a seasonal segmentation aggregation algorithm to obtain second daily load curves corresponding to different seasons.
In response examiner respectfully disagree. The claimed seasonal segmentation is nothing but segmenting collected data. Since Yang teaches classifying data points into classes (i.e. seasonal segmentation) and selecting class-I daily load curves based on the clustering result (see paragraph [0080], [0090]) which satisfy the arguing limitation.
(2) Applicant argues Chen fails to disclose separately comparing a target daily load curve with daily load curve models corresponding to different load types to obtain a candidate classification result corresponding to the target daily load curve, wherein the target daily load curve is any one of the first daily load curve and the second daily load curves.
In response examiner respectfully disagree. Chan teaches (paragraph [0044], [0061]: a classification prediction is performed on a grid supply load daily curve corresponding to the grid supply load characteristic trend prediction result based on the grid supply load characteristic trend prediction result. In comparison with the existing similar day selection algorithm which determines the grid supply load curve type, it can select the grid supply load curve type that meets the trend of grid supply load).
(3) Applicant argues Chen fails to disclose determining separate Euclidean Distances between the target daily load curve and the daily load curve models corresponding to the different load types; and determining a load type of a daily load curve model corresponding to a minimum Euclidean Distance among the determined Euclidean Distances as the candidate classification result corresponding to the target daily load curve.
In response examiner respectfully disagree. Chan teaches the arguing limitation of : determining separate Euclidean Distances between the target daily load curve and the daily load curve models corresponding to the different load types; and determining a load type of a daily load curve model corresponding to a minimum Euclidean Distance among the determined Euclidean Distances as the candidate classification result corresponding to the target daily load curve (paragraph [0029]-[0050]: obtaining a target prediction day and a historical data set of a grid supply load of the target electrical grid and daily characteristic data of target influencing factor corresponding to the target prediction day, then determining a characteristic data set of a historical grid supply load based on the historical data set of the grid supply load).
(4) Applicant argues Yang fails to disclose a daily load curve model corresponding to each load type comprises a first model obtained after historical load data under the corresponding load type is trained according to the cluster algorithm and second models corresponding to the different seasons obtained after the historical load data is trained according to the seasonal segmentation aggregation algorithm and the first daily load curve is compared with the first model, and a second daily load curve among the second daily load curves is compared with a second model corresponding to a same season among the second models.
In response examiner respectfully disagree. Yang teaches the arguing limitation of a daily load curve model corresponding to each load type comprises a first model obtained after historical load data under the corresponding load type is trained according to the cluster algorithm and second models corresponding to the different seasons obtained after the historical load data is trained according to the seasonal segmentation aggregation algorithm and the first daily load curve is compared with the first model, and a second daily load curve among the second daily load curves is compared with a second model corresponding to a same season among the second models (paragraph [0024]-[0042], [0077], [0090]-[0095]: classifying the data points into classes that include cluster centers and selecting class-I daily load curves based on the clustering result and optimizing and identifying static load models corresponding to the N load curves).
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, 4-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Regarding the claim 1, it recites clustering and averaging to-be-classified load data acquired through the power system according to a cluster algorithm based on a Pearson correlation coefficient to obtain a first daily load curve, wherein the to-be-classified load data comprises a daily load curve in years; segmenting and averaging the to-be-classified load data acquired through the power system according to a seasonal segmentation aggregation algorithm to obtain second daily load curves corresponding to different seasons; separately comparing a target daily load curve with daily load curve models corresponding to different load types to obtain a candidate classification result corresponding to the target daily load curve, wherein the target daily load curve is any one of the first daily load curve and the second daily load curves; and determining a target classification result corresponding to the to-be-classified load data according to the candidate classification result; wherein separately comparing the target daily load curve with the daily load curve models corresponding to the different load types to obtain the candidate classification result corresponding to the target daily load curve comprises: determining separate Euclidean Distances between the target daily load curve and the daily load curve models corresponding to the different load types; and determining a load type of a daily load curve model corresponding to a minimum Euclidean Distance among the determined Euclidean Distances as the candidate classification result corresponding to the target daily load curve, wherein: a daily load curve model corresponding to each load type comprises a first model obtained after historical load data under the corresponding load type is trained according to the cluster algorithm and second models corresponding to the different seasons obtained after the historical load data is trained according to the seasonal segmentation aggregation algorithm; and the first daily load curve is compared with the first model, and a second daily load curve among the second daily load curves is compared with a second model corresponding to a same season among the second models.
The limitations separately comparing, determining a target classification result, comparing the target daily load, determining separate Euclidean Distances, determining a load type of a daily load, compared with the first model, and a second daily load curve as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and it’s mathematical concept). User can mentally compare and determine data as claimed. If necessary user can use physical aid such as pen and paper. Hence, these limitations are a mental process. See MPEP 2106.04(a)(2) III, B, If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand.").
The claim recites two additional elements: clustering and averaging to-be-classified load data according to a cluster algorithm based on a Pearson correlation coefficient to obtain a first daily load curve, wherein the to-be-classified load data comprises a daily load curve in years; segmenting and averaging the to-be-classified load data according to a seasonal segmentation aggregation algorithm to obtain second daily load curves corresponding to different seasons. The clustering, averaging, and segmenting steps as recited amounts to mere data gathering and organizing, which is a form of insignificant extra-solution activity, (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to the abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of clustering, averaging, and segmenting steps amounts to no more than mere instructions to apply the exception using a generic computer component. The courts have recognized these functions as well‐understood, routine, and conventional as they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d) II, 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 (utilizing an intermediary computer to forward information)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim 4 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of classifying electrical load data. The claim recites the limitations of wherein determining the target classification result corresponding to the to-be-classified load data according to the candidate classification result comprises: determining a load type with a highest frequency of occurrence in the candidate classification result as the target classification result corresponding to the to-be-classified load data, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 5 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of classifying electrical load data. The claim recites the limitations of wherein clustering and averaging the to-be-classified load data according to the cluster algorithm based on the Pearson correlation coefficient to obtain the first daily load curve comprises: clustering the to-be-classified data according to the cluster algorithm based on the Pearson correlation coefficient to obtain a plurality of clusters; and averaging a cluster comprising the most daily load curves among the plurality of clusters to obtain the first daily load curve, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
Claim 6 is dependent on claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of classifying electrical load data. The claim recites the limitations of wherein segmenting and averaging the to-be-classified load data according to the seasonal segmentation aggregation algorithm to obtain the second daily load curves corresponding to the different seasons comprises: segmenting the to-be-classified load data according to the seasonal segmentation aggregation algorithm and the different seasons to obtain segments corresponding to the different seasons; and separately averaging daily load curves comprised in the segments corresponding to the different seasons to obtain the second daily load curves corresponding to the different seasons, which can be done mentally with or without the use of a physical aid (e.g., pen and paper) or with a generic computer and is not an inventive concept that meaningfully limits the abstract idea. Therefore, the limitation is a mental process.
As to claims 7-9, they have similar limitations as of claim 1 above. Hence, they are rejected under the same rational as of claim 1 above.
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.
Claims 1-9 are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. (Pub. No. : US 20210109140 A1) in the view of Chen et al. (Pub. No. : US 20240063637 A1) and Qin et al. (Pu. No. : CN 117034066 A).
As to claim 1 Yang teaches a method for classifying electrical loads, comprising:
clustering and averaging to-be-classified load data acquired through the power system according to a cluster algorithm, wherein the to-be-classified load data comprises a daily load curve in years (paragraph [0080]: Calculate Euclidean distances from N data points to the h cluster centers one by one, and classify the data points into classes that include cluster centers with minimum distances to the data points);
segmenting and averaging the to-be-classified load data acquired through the power system according to a seasonal segmentation aggregation algorithm to obtain second daily load curves corresponding to different seasons (paragraph [0023]-[0025]: selecting h data points as initial cluster centers and after classifying the N data points, separately calculating means of data points in h classes);
determining a target classification result corresponding to the to-be-classified load data according to the candidate classification result (paragraph [0009]: classifying loads by using the K-means algorithm based on the load data obtained in step 1, wherein loads with a similar shape are classified into one class based on Euclidean distances);
wherein: a daily load curve model corresponding to each load type comprises a first model obtained after historical load data under the corresponding load type is trained according to the cluster algorithm and second models corresponding to the different seasons obtained after the historical load data is trained according to the seasonal segmentation aggregation algorithm and the first daily load curve is compared with the first model, and a second daily load curve among the second daily load curves is compared with a second model corresponding to a same season among the second models (paragraph [0024]-[0042], [0077], [0090]-[0095]: classifying the data points into classes that include cluster centers and selecting class-I daily load curves based on the clustering result and optimizing and identifying static load models corresponding to the N load curves).
Yang does not explicitly disclose but Chen teaches separately comparing a target daily load curve with daily load curve models corresponding to different load types to obtain a candidate classification result corresponding to the target daily load curve, wherein the target daily load curve is any one of the first daily load curve and the second daily load curves (paragraph [0044], [0061]: a classification prediction is performed on a grid supply load daily curve corresponding to the grid supply load characteristic trend prediction result based on the grid supply load characteristic trend prediction result. In comparison with the existing similar day selection algorithm which determines the grid supply load curve type, it can select the grid supply load curve type that meets the trend of grid supply load);
wherein separately comparing the target daily load curve with the daily load curve models corresponding to the different load types to obtain the candidate classification result corresponding to the target daily load curve comprises: determining separate Euclidean Distances between the target daily load curve and the daily load curve models corresponding to the different load types; and determining a load type of a daily load curve model corresponding to a minimum Euclidean Distance among the determined Euclidean Distances as the candidate classification result corresponding to the target daily load curve (paragraph [0029]-[0050]: obtaining a target prediction day and a historical data set of a grid supply load of the target electrical grid and daily characteristic data of target influencing factor corresponding to the target prediction day, then determining a characteristic data set of a historical grid supply load based on the historical data set of the grid supply load).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Yang by adding above limitation as taught by Chen for reducing the amount of data processing, preventing overfitting, and improving the accuracy (Chen, paragraph [0042]).
Yang and Chen do not explicitly disclose but Qin teaches based on a Pearson correlation coefficient to obtain a first daily load curve (page 3, 6th paragraph: calculating the correlation degree between any two daily load curves according to the Pearson correlation coefficient formula). It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Yang and Chen by adding above limitation as taught by Qin to improves the precision of the reliability of each time section (Qin, abstract).
As to clam 4 Yang together with Chen and Qin teaches a method according to claim 1. Yang teaches wherein determining the target classification result corresponding to the to-be-classified load data according to the candidate classification result comprises: determining a load type with a highest frequency of occurrence in the candidate classification result as the target classification result corresponding to the to-be-classified load data (paragraph [0003]).
As to clam 5 Yang together with Chen and Qin teaches a method according to claim 1. Yang teaches wherein clustering and averaging the to-be-classified load data according to the cluster algorithm based on the Pearson correlation coefficient to obtain the first daily load curve comprises: clustering the to-be-classified data according to the cluster algorithm based on the Pearson correlation coefficient to obtain a plurality of clusters; and averaging a cluster comprising the most daily load curves among the plurality of clusters to obtain the first daily load curve (Yang, paragraph [0080], Qin page 3).
As to clam 6 Yang together with Chen and Qin teaches a method according to claim 1. Yang teaches wherein segmenting and averaging the to-be-classified load data according to the seasonal segmentation aggregation algorithm to obtain the second daily load curves corresponding to the different seasons comprises: segmenting the to-be-classified load data according to the seasonal segmentation aggregation algorithm and the different seasons to obtain segments corresponding to the different seasons; and separately averaging daily load curves comprised in the segments corresponding to the different seasons to obtain the second daily load curves corresponding to the different seasons (paragraph [0025]).
As to claims 7-9, they have similar limitations as of claim 1 above. Hence, they are rejected under the same rational as of claim 1 above.
Examiner's Note: Examiner has cited particular columns and line numbers or paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in its entirety as potentially teaching of all or part of the claimed invention, as well as the context.
Conclusion
THIS ACTION IS MADE FINAL. 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.
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MD I UDDIN whose telephone number is (571)270-3559. The examiner can normally be reached M-F, 8:00 am to 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sherief Badawi can be reached at 571-272-9782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MD I UDDIN/Primary Examiner, Art Unit 2169