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 .
This office action is in response to Pre-Amendment filed on Oct. 24, 2023.
Claim 1 has been canceled.
New claims 2-31 have been added.
Claim Objections
Claim 25 is objected to because of the following informalities: “The non-transitory computer-readable storage medium of claim 25” should be changed to -- The non-transitory computer-readable storage medium of claim 24 --. Appropriate correction is required.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP §§ 706.02(l)(1) - 706.02(l)(3) for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 2, 17, 24 and 28 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 23 and 24 respectively of U.S. Patent No. 10,296,843. Although the claims at issue are not identical, they are not patentably distinct from each other because the patent claims include all the limitations of the instant application claims, respectively (see table below). The patent claims also include additional limitations. Hence, the instant application claims are generic to the species of invention covered by the respective patent claims. As such, the instant application claims are anticipated by the patent claims and are therefore not patentably distinct therefrom (See Eli Lilly and Co. v. Barr Laboratories Inc., 58 USPQ2D 1869, " a later genus claim limitation is anticipated by, and therefore not patentably distinct from, an earlier species claim", In re Goodman, 29 USPQ2d 2010, "Thus, the generic invention is 'anticipated' by the species of the patented invention" and the instant “application claims are generic to species of invention covered by the patent claim, and since without terminal disclaimer, extant species claim preclude issuance of generic application claims”).
Claims 3-16, 18-23, 25-27 and 29-31 are rejected as being dependent on independent claims 2, 17, 24 and 28.
Application 18/491,875
U.S. Patent No. 10,296,843
Claims 2 &17.
A method comprising:
determining, by one or more processors, signal values for a selected set of signals relating to a plurality of energy usage conditions, wherein the selected set of signals includes a consumption drop signal corresponding to a drop in energy consumption measured by an energy meter compared to an average energy consumption measured by the energy meter;
generating, by the one or more processors based on the determined signals values, a plurality of N-dimensional representations for the plurality of energy usage conditions, wherein each N-dimensional representation corresponds to an energy usage condition and includes a dimension corresponding to a signal value determined for the consumption drop signal; and
applying, by the one or more processors, a trained machine learning classifier model to the plurality of N-dimensional representations to identify energy usage conditions associated with nontechnical loss, wherein energy usage conditions associated with non-technical loss are associated with irregular energy usage.
Claim 1.
A computer-implemented method for energy management, comprising:
importing two or more different types of energy or customer-related data from a plurality of data sources; obtaining signal values for a set of signals based on the imported data, wherein the set of signals relates to a plurality of energy usage conditions;
generating, based on the signal values, a plurality of N-dimensional representations for the plurality of energy usage conditions, wherein each of said plurality of N-dimensional representations (a) is generated based on the signal values associated with a respective energy usage condition selected from said plurality of energy usage conditions, and (b) has a number of dimensions indicated by N, said number of dimensions corresponding to a number of signals associated with said respective energy usage condition;
applying at least one machine learning algorithm to the plurality of N-dimensional representations to create a classifier model that is used to identify one or more energy usage conditions associated with non-technical loss of the energy, wherein the at least one machine learning algorithm is selected from the group consisting of a boosted decision tree, a classification tree, a regression tree, a bagging tree, a random forest, a neural network, and a rotational forest;
providing one or more new signal values associated with new usage conditions to the classifier model; re-evaluating a relevance or importance of each signal from the set of signals for identifying the non-technical loss based on the one or more new signal values; and training the classifier model by (1) modifying the classifier model to account for more relevant new signal values and new usage conditions, and/or (2) selectively eliminating one or more less relevant signals in the identification of the non-technical loss...
Claim 24
A non-transitory computer readable medium storing instruction that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
determining signal values for a selected set of signals relating to a plurality of energy usage conditions, wherein the selected set of signals includes a consumption drop signal corresponding to a drop in energy consumption measured by an energy meter compared to an average energy consumption measured by the energy meter;
generating, based on the determined signals values, a plurality of N-dimensional representations for the plurality of energy usage conditions, wherein each N-dimensional representation corresponds to an energy usage condition and includes a dimension corresponding to a signal value determined for the consumption drop signal; and
applying a trained machine learning classifier model to the plurality of N-dimensional representations to identify energy usage conditions associated with nontechnical loss, wherein energy usage conditions associated with non-technical loss are associated with irregular energy usage.
Claim 24
A non-transitory computer-readable storage medium including instructions that, when executed by a server, cause the server to perform operations comprising: importing two or more different types of energy or customer-related data from a plurality of data sources; obtaining signal values for a set of signals based on the imported data, wherein the set of signals relates to a plurality of energy usage conditions;
generating, based on the signal values, a plurality of N-dimensional representations for the plurality of energy usage conditions, wherein each of said plurality of N-dimensional representations (a) is generated based on the signal values associated with a respective energy usage condition selected from said plurality of energy usage conditions, and (b) has a number of dimensions indicated by N, said number of dimensions corresponding to a number of signals associated with said respective energy usage condition; applying at least one machine learning algorithm to the plurality of N-dimensional representations to create a classifier model that is used to identify one or more energy usage conditions associated with non-technical loss of the energy, wherein the at least one machine learning algorithm is selected from the group consisting of a boosted decision tree, a classification tree, a regression tree, a bagging tree, a random forest, a neural network, and a rotational forest; providing one or more new signal values associated with new usage conditions to the classifier model; re-evaluating a relevance or importance of each signal from the set of signals for identifying the non-technical loss based on the one or more new signal values; and training the classifier model by (1) modifying the classifier model to account for more relevant new signal values and new usage conditions, and/or (2) selectively eliminating one or more less relevant signals in the identification of the non-technical loss.
Claim 28
A system comprising: a memory; and one or more processors communicatively coupled to the memory, the one or more processors configured to:
determine signal values for a selected set of signals relating to a plurality of energy usage conditions, wherein the selected set of signals includes a consumption drop signal corresponding to a drop in energy consumption measured by an energy meter compared to an average energy consumption measured by the energy meter;
generate, based on the determined signals values, a plurality of N-dimensional representations for the plurality of energy usage conditions, wherein each N-dimensional representation corresponds to an energy usage condition and includes a dimension corresponding to a signal value determined for the consumption drop signal; and
apply a trained machine learning classifier model to the plurality of N-dimensional representations to identify energy usage conditions associated with nontechnical loss, wherein energy usage conditions associated with non-technical loss are associated with irregular energy usage.
Claim 23
A system for energy management, comprising: a server in communication with a plurality of data sources; and a memory storing instructions that, when executed by the server, cause the server to perform operations comprising: importing two or more different types of energy or customer-related data from the plurality of data sources; obtaining signal values for a set of signals based on the imported data, wherein the set of signals relates to a plurality of energy usage conditions;
generating, based on the signal values, a plurality of N-dimensional representations for the plurality of energy usage conditions, wherein each of said plurality of N-dimensional representations (a) is generated based on the signal values associated with a respective energy usage condition selected from said plurality of energy usage conditions, and (b) has a number of dimensions indicated by N, said number of dimensions corresponding to a number of signals associated with said respective energy usage condition;
applying at least one machine learning algorithm to the plurality of N-dimensional representations to create a classifier model that is used to identify one or more energy usage conditions associated with non-technical loss of the energy, wherein the at least one machine learning algorithm is selected from the group consisting of a boosted decision tree, a classification tree, a regression tree, a bagging tree, a random forest, a neural network, and a rotational forest; providing one or more new signal values associated with new usage conditions to the classifier model; re-evaluating a relevance or importance of each signal from the set of signals for identifying the non-technical loss based on the one or more new signal values; and training the classifier model by (1) modifying the classifier model to account for more relevant new signal values and new usage conditions, and/or (2) selectively eliminating one or more less relevant signals in the identification of the non-technical loss.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 2-20, and 24-31 are rejected under 35 U.S.C. 102(1) as being anticipated by Non-Technical Loss Analysis for Detection of Electricity Theft using Support Vector Machines ("Nagi").
Regarding claims 2, 17, 24 and 28, Nagi discloses a method comprising: determining, by one or more processors, signal values for a selected set of signals relating to a plurality of energy usage conditions, wherein the selected set of signals includes a consumption drop signal corresponding to a drop in energy consumption measured by an energy meter compared to an average energy consumption measured by the energy meter (See Nagi; e.g., at Fig. 1; and at P. 909: B. Customer Filtering and Selection: discloses obtaining different types of customer-related data; C. Feature Extraction: discloses a plurality of consumption features h (i.e., usage conditions)); generating, by the one or more processors based on the determined signals values, a plurality of N-dimensional representations for the plurality of energy usage conditions, wherein each N-dimensional representation corresponds to an energy usage condition and includes a dimension corresponding to a signal value determined for the consumption drop signal (See Nagi; e.g., at Fig. 1; and at p. 909 (C. Feature Extraction): discloses vectors x(m) = {xⁿ(m), h = 1 H} (i.e., a plurality of N-dimensional representations) where each h is a dimension); and applying, by the one or more processors, classifier model to the plurality of N-dimensional representations to identify energy usage conditions associated with nontechnical loss (See Nagi; e.g., at Fig. 1: discloses training an SVM-based model; p. 909 (C. Feature Extraction): each load profile is characterized by a vector), wherein energy usage conditions associated with non-technical loss are associated with irregular energy usage (See Nagi; e.g., at p. 907, col. 2: discloses other ML methods that can be used).
Regarding claim 3, Nagi further comprising: applying, by the one or more processors, at least one machine learning algorithm to the plurality of N-dimensional representations to produce the classifier model for identifying nontechnical loss (See Nagi; e.g., at Fig. 1: discloses training an SVM-based model; p. 909 (C. Feature Extraction): each load profile is characterized by a vector).
Regarding claim 4, Nagi further comprising selecting, by the one or more processors, the selected set of signals relating to the plurality of energy usage conditions (See Nagi; e.g., at Fig. 1: discloses training an SVM-based model; p. 909 (C. Feature Extraction): each load profile is characterized by a vector).
Regarding claim 5, Nagi further discloses wherein N represents a number of signals in the selected set of signals (See Nagi; e.g., at Fig. 1; and at p. 909 (C. Feature Extraction): discloses vectors x(m) = {xⁿ(m), h = 1 H} (i.e., a plurality of N-dimensional representations) where each h is a dimension).
Regarding claim 6, Nagi further discloses wherein the trained classifier model is configured to classify, as corresponding to non-technical loss a first portion of the plurality of N-dimensional representations within an allowable N-dimensional proximity from N-dimensional representations which have been previously recognized as corresponding to non-technical loss (See Nagi; e.g., at Fig. 1; and at P. 908: II. Support Vector Machine).
Regarding claim 7, Nagi further discloses wherein the trained classifier model is further configured to classify, as corresponding to normal energy usage, at least a second portion of the plurality of N-dimensional representations within an allowable N-dimensional proximity from N-dimensional representations which have been previously recognized as corresponding to normal energy usage. (See Nagi; e.g., at Fig. 1: SVM training); Fig. 2: SVM Training and Test Data; and at p. 908: II. Support Vector Machine).
Regarding claim 8, Nagi further discloses wherein the trained machine learning classifier model is trained with at least one of a support vector machine, a boosted decision tree, a classification tree, a regression tree, a bagging tree, a random forest, a neural network, or a rotational forest (See Nagi; e.g., at p. 907, col. 2: discloses other ML methods that can be used).
Regarding claim 9, Nagi further discloses wherein applying the trained machine learning classifier model to the plurality of N-dimensional representations to identify energy usage conditions associated with non-technical loss comprises: identifying, by the one or more processors, a plurality of energy usage conditions that have likelihoods of being associated with non-technical loss (See Nagi: e.g., p. 909: D. Data Preprocessing; and at p. 910, col. 1, last paragraph); and ranking, by the one or more processors, the plurality of energy usage conditions based on the likelihoods of being associated with non-technical loss. See Nagi: e.g., p. 909: D. Data Preprocessing; and at p. 910, col. 1, last paragraph).
Regarding claim 10, Nagi further comprising: determining, by the one or more processors, that at least some of a plurality of meters meet specified ranking threshold criteria, the plurality of meters including the meter (See Nagi: e.g., p. 909: D. Data Preprocessing; and at p. 910, col. 1, last paragraph); and identifying, by the one or more processors, the at least some of the plurality of utility meters as candidates for investigation(See Nagi; e.g., P. 909: D. Data Preprocessing; and at P. 910, col. 1, last paragraph).
Regarding claim 11, Nagi further discloses wherein one or more signals in the selected set of signals are associated with at least one of an account attribute signal category, an anomalous load signal category, a calculated status signal category, a current analysis signal category, a missing data signal category, a disconnected signal category, a meter event signal category, a monthly meter anomalous load signal category, a monthly meter consumption on inactive signal category, an outage signal category, a stolen meter signal category, an unusual production signal category, a work order signal category, or a zero reads signal category (See Nagi; e.g., p. 909: B. Customer Filtering and Selection, and C. Feature Extraction).
Regarding claim 12, Nagi further comprising: acquiring, by the one or more processors, a set of formulas corresponding to the selected set of signals, each formula in the set of formulas corresponding to a respective signal in the selected set of signals(See Nagi; e.g., p. 909: E. Data Normalization); and determining, by the one or more processors, the signal values for the selected set of signals based on the set of formulas (See Nagi; e.g., p. 909: E. Data Normalization).
Regarding claim 13, Nagi further discloses wherein one or more signals in the selected set of signals is based on an analysis of active and reactive power data. (See Nagi; e.g., p. 909: B. Customer Filtering and Selection, and C. Feature Extraction).
Regarding claim 14, Nagi further discloses wherein the one or more signals based on an analysis of active and reactive power data characterize irregular variations in year-over-year consumption patterns (See Nagi; e.g., p. 909: B. Customer Filtering and Selection, and C. Feature Extraction).
Regarding claim 15, Nagi further comprising reporting, by the one or more processors, the identified energy usage conditions associated with non-technical loss (See Nagi; e.g., at Fig.1).
Regarding claim 16, Nagi further discloses wherein reporting the identified energy usage conditions associated with non-technical loss comprises graphically rendering a report of the identified energy usage condition for presentation by a computer system (See Nagi; e.g., p. 907, 2ⁿᵈ col.: "The approach proposed in this paper provides an intelligent system for assisting TNB inspection teams to increase effectiveness of their operation in reducing NTLs, and detecting fraudulent consumers based on load profiles of customers derived from the customer database. This system will increase fraud detection hit-rate for onsite inspection and reduce operational costs due to onsite inspection in monitoring NTL activities.").
Regarding claims 25-27 and 29-31 recite limitations substantially similar to the claims 6-7 and 14. Therefore, these claims were rejected for similar reasons as stated above.
Allowable Subject Matter
Claims 21-23 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding 21-23, Nagi discloses all the subject matters above. However, Nagi fails to teach all the limitations in claims 21-23
Examiner's Note: Examiner has cited particular columns and line numbers in the references 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 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 entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
When responding to this Office Action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CongVan Tran whose telephone number is (571)272-7871. The examiner can normally be reached on Mon-Th.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alison Slater can be reached on (571) 270-0375. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
PNG
media_image1.png
75
75
media_image1.png
Greyscale
UNITED STATES PATENT AND TRADEMARK OFFICE
/CONGVAN TRAN/Primary Examiner, Art Unit 2647
PNG
media_image2.png
919
706
media_image2.png
Greyscale
PNG
media_image3.png
921
712
media_image3.png
Greyscale
PNG
media_image4.png
924
713
media_image4.png
Greyscale
PNG
media_image5.png
920
715
media_image5.png
Greyscale
PNG
media_image6.png
915
690
media_image6.png
Greyscale
PNG
media_image7.png
910
712
media_image7.png
Greyscale