DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on 03/30/2026 has been entered. Claim(s) 21-24, 27-34, 37-40 is/are now pending in the application. Applicant's amendments have addressed all informalities as previously set forth in the non-final action mailed on 12/29/2025.
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.
Claim(s) 21-24, 27-34, 37-40 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more (See 2019 Update: Eligibility Guidance).
Independent Claim(s) 21, 31 recites
use one or machine learning algorithms to
predict anomalous information using one or more data models to
represent
a denormalized first data having a first update frequency and
a normalized second data having a second update frequency greater than the first update frequency,
determine the denormalized first data in real-time or near real-time based on a received continuous data stream by aggregating periodic energy usage values into aggregated energy usage values represented by the first data
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)].
In combination with Independent Claim 21, 31, Claim(s) 22-24, 27-30, 32-34, 37-40 recite(s)
wherein
the one or more machine learning algorithms are trained to
receive data in accordance with a first data model.
determine the denormalized first data in accordance with the first data model; and
determine the normalized second data in accordance with the first data model.
determine the normalized second data by
correcting aberrational values of energy usage represented by the second data.
store the first data having the first update frequency; and
store the second data having the second update frequency.
wherein
the one or more machine learning algorithms are applied directly to the first data while stored in the relational database, the second data while stored in the non-relational database, or a combination thereof.
wherein
the denormalized first data, the normalized second data, or a combination thereof is persisted based one the predicted anomalous information.
predict energy theft, abnormal energy usage events, or a combination thereof based on the anomalous information
[Mathematical Concepts – mathematical relationships; mathematical formulas or equations or mathematical calculation] and/or [Mental Processes - concepts performed in the human mind (including an observation, evaluation, judgement, opinion)].
This judicial exception is not integrated into a practical application. Limitations that are not indicative of integration into a practical application:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)) (i.e. one or more processors to use one or machine learning algorithms to; wherein the system further comprises: a relational database to; a non-relational database to);
Adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)) (i.e. generic/conventional computing functions (i.e. generic data acquisition, storage, output, display, etc.)); or
Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP § 2106.05(h)) (i.e.).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because looking at the additional elements as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. The additional elements simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)) (i.e. See Alice Corp. and cited references for evidence of additional elements (i.e., generic computer structure)).
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 21-24, 29-34, 39, 40 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by GARRITY ET AL. (US 20140074670 A1) (hereinafter “GARRITY”).
With respect to Claim(s) 21, 31, GARRITY teaches an analytics method and system corresponding to energy meters and the BRI of:
one or more processors (See, e.g., Fig(s). 1-3)
to use
one or machine learning algorithms (See, e.g., ¶ 0028)
to
predict anomalous information using one or more data models (See, e.g., ¶ 0028)
to represent
a first denormalized data having a first update frequency (See, e.g., ¶ 0024 customer information…including energy usage information | POSITA would understand this to slow-changing non-normalized raw data in light of the specification as originally filed and meet the BRI of the claim language.)
and
a normalized second data having a second update frequency greater than the first update frequency (See, e.g., ¶ 0017, 0024, 0029, 0030 changes in electric load demand to a meter…meter data…load profile (i.e., an electrical load variation over a time interval)…temperature normalization…consumption patterns | POSITA would understand this to be dynamically changing data correlating to corrected aberrational (normalized) values of energy usage in light of the specification as originally filed and meet the BRI of the claim language.),
wherein
the one or more processors
determine the denormalized first data in real-time or near real-time based on a received continuous data stream by aggregating periodic energy usage values into aggregated energy usage values represented by the first data (See, e.g., ¶ 0024 customer information…including energy usage information | POSITA would understand this to slow-changing non-normalized raw data in light of the specification as originally filed and meet the BRI of the claim language.; ¶ 0017, 0024, 0029, 0030 changes in electric load demand to a meter…meter data…load profile (i.e., an electrical load variation over a time interval)…temperature normalization…consumption patterns).
With respect to Claim(s) 22, 32, GARRITY teaches the BRI of the parent claim(s).
GARRITY further teaches the BRI of:
wherein
the one or more machine learning algorithms are trained to
receive data in accordance with a first data model (See, e.g., ¶ 0028).
With respect to Claim(s) 23, 33, GARRITY teaches the BRI of the parent claim(s).
GARRITY further teaches the BRI of:
wherein
the one or more processors further:
determine the denormalized first data in accordance with the first data model (See, e.g., ¶ 0024 customer information…including energy usage information | POSITA would understand this to slow-changing non-normalized raw data in light of the specification as originally filed and meet the BRI of the claim language.); and
determine the normalized second data in accordance with the first data model (See, e.g., ¶ 0017, 0024, 0029, 0030 changes in electric load demand to a meter…meter data…load profile (i.e., an electrical load variation over a time interval)…temperature normalization…consumption patterns | POSITA would understand this to be dynamically changing data correlating to corrected aberrational (normalized) values of energy usage in light of the specification as originally filed and meet the BRI of the claim language.).
With respect to Claim(s) 24, 34, GARRITY teaches the BRI of the parent claim(s).
GARRITY further teaches the BRI of:
wherein
the one or more processors
determine the normalized second data by
correcting aberrational values of energy usage represented by the second data (See, e.g., ¶ 0017, 0024, 0029, 0030 changes in electric load demand to a meter…meter data…load profile (i.e., an electrical load variation over a time interval)…temperature normalization…consumption patterns | POSITA would understand this to be dynamically changing data correlating to corrected aberrational (normalized) values of energy usage in light of the specification as originally filed and meet the BRI of the claim language.).
With respect to Claim(s) 29, 39, GARRITY teaches the BRI of the parent claim(s).
GARRITY further teaches the BRI of:
wherein
the denormalized first data, the normalized second data, or a combination thereof is persisted based one the predicted anomalous information (See, e.g., ¶ 0019-0021, 0024; See also, e.g., Fig(s). 1-3).
With respect to Claim(s) 30, 40, GARRITY teaches the BRI of the parent claim(s).
GARRITY further teaches the BRI of:
wherein
the one or more processors further
predict energy theft, abnormal energy usage events, or a combination thereof based on the anomalous information (See, e.g., ¶ 0028).
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.
Claim(s) 27, 29, 37, 38 is/are rejected under 35 U.S.C. 103 as being unpatentable over the cited reference(s) of the parent claim(s) in view of HARRISON ET AL. (US 9886483 B1) (hereinafter “HARRISON”).
With respect to Claim(s) 27, 37, GARRITY teaches the BRI of the parent claim(s).
GARRITY further teaches the BRI of:
wherein
the system further comprises:
a database (See, e.g., ¶ 0019-0021, 0024; See also, e.g., Fig(s). 1-3)
to
store the first data having the first update frequency (See, e.g., ¶ 0024 customer information…including energy usage information | POSITA would understand this to slow-changing non-normalized raw data in light of the specification as originally filed and meet the BRI of the claim language.);
and
a database (See, e.g., ¶ 0019-0021, 0024; See also, e.g., Fig(s). 1-3)
to
store the second data having the second update frequency (See, e.g., ¶ 0017, 0024, 0029, 0030 changes in electric load demand to a meter…meter data…load profile (i.e., an electrical load variation over a time interval)…temperature normalization…consumption patterns | POSITA would understand this to be dynamically changing data correlating to corrected aberrational (normalized) values of energy usage in light of the specification as originally filed and meet the BRI of the claim language.).
However, GARRITY is lacking the explicit language of:
relational database, non-relational database.
HARRISON teaches a method and system corresponding to SQL and non-relational databases and the BRI of:
utilizing relational database(s), non-relational database(s) (See Fig. 1A; See also Figs. 1B, 2, 6, 9).
It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify GARRITY to include utilizing relational database(s), non-relational database(s).
One of ordinary skill in the art would have been motivated to modify GARRITY because it would be beneficial to perform join or similar operations between a relational table in one data store and a data object in another data store. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results.
With respect to Claim(s) 28, 38, GARRITY teaches the BRI of the parent claim(s).
GARRITY further teaches the BRI of:
wherein
the one or more machine learning algorithms are applied directly to the first data while stored in the database, the second data while stored in the database, or a combination thereof (See, e.g., ¶ 0028).
HARRISON further teaches the BRI of:
utilizing relational database(s), non-relational database(s) (See Fig. 1A; See also Figs. 1B, 2, 6, 9).
It would have been obvious to one ordinary skill in the art, at the time before the effective filing date of the claimed invention, to modify GARRITY to include utilizing relational database(s), non-relational database(s).
One of ordinary skill in the art would have been motivated to modify GARRITY because it would be beneficial to perform join or similar operations between a relational table in one data store and a data object in another data store. Further, it would be obvious to combine prior art elements according to known methods to yield predictable results, simply substitute one known element for another to obtain predictable results, use known techniques to improve similar devices in the same way, and/or apply a known technique to a known device ready for improvement to yield predictable results.
Response to Arguments
Applicant’s amendments, filed on 03/30/2026, have been entered and fully considered. In light of the applicant’s amendments changing the scope of the claimed invention, the rejection(s) have been withdrawn or updated. However, upon further consideration, a new or updated ground(s) of rejection(s) have been made, and applicant's argument(s)/remark(s) pertaining to the amended language have been rendered moot.
Applicant's argument(s)/remark(s), see page(s) 5, filed 03/30/2026, with respect to the 101 rejection(s) has/have been fully considered.
-Applicant states
“Response to 35 U.S.C. $ 101 Rejections
In response, to advance prosecution without conceding to the rejections, the independent claims have been amended to include at least one limitation that precludes the claims from practically being performed mentally, manually, or by a generic computer. Support for these amendments may be found at least in canceled claims 25 and 26.
Specifically, independent claim 21 has been amended to require that the one or more processors determine the denormalized first data in real-time or near real-time based on a received continuous data stream by aggregating periodic energy usage values into aggregated energy usage values represented by the first data. Each of the other claims, by amendment or dependence, includes a corresponding limitation.”.
Examiner respectfully disagrees with the underlined argument(s)/remark(s).
Said limitation was already previously rejected under 101. Examiner maintains the 101 rejection(s).
Applicant's argument(s)/remark(s), see page(s) 5-6, filed 03/30/2026, with respect to the art rejection(s) has/have been fully considered.
-Applicant states
“Response to 35 U.S.C. $ 102 Rejections
In response, to advance prosecution without conceding to the rejections, the independent claims have been amended to include at least one limitation not disclosed by Garrity. Support for these amendments may be found at least in canceled claims 25 and 26.
Specifically, independent claim 21 has been amended to require that the one or more processors determine the denormalized first data in real-time or near real-time based on a received continuous data stream by aggregating periodic energy usage values into aggregated energy usage values represented by the first data. Each of the other claims, by amendment or dependence, includes a corresponding limitation.
Garrity does not describe determining denormalized first data in real-time or near real- time based on a received continuous data stream by aggregating periodic energy usage values into aggregated energy usage values represented by the first data. The examiner's argument, regarding canceled claim 25, that Garrity teaches aggregating periodic energy usage values into aggregated energy usage values represented by the first data is traversed because no such teaching or suggestion by Garrity is found.”.
Examiner respectfully disagrees with the underlined argument(s)/remark(s).
GARRITY teaches an analytics method and system corresponding to energy meters and the BRI of:
…
a first denormalized data having a first update frequency (See, e.g., ¶ 0024 customer information…including energy usage information | POSITA would understand this to slow-changing non-normalized raw data in light of the specification as originally filed and meet the BRI of the claim language.)
…
determine the denormalized first data in real-time or near real-time based on a received continuous data stream by aggregating periodic energy usage values into aggregated energy usage values represented by the first data (See, e.g., ¶ 0024 customer information…including energy usage information | POSITA would understand this to slow-changing non-normalized raw data in light of the specification as originally filed and meet the BRI of the claim language.; ¶ 0017, 0024, 0029, 0030 changes in electric load demand to a meter…meter data…load profile (i.e., an electrical load variation over a time interval)…temperature normalization…consumption patterns).
-Applicant states
“Response to 35 U.S.C. $ 103 Rejections
Combining Harrison with Garrity does not add the missing limitation, as discussed above, to Garrity.”.
Examiner respectfully disagrees with the underlined argument(s)/remark(s).
See above response.
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 extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAYMOND NIMOX whose telephone number is (469)295-9226. The examiner can normally be reached Mon-Thu 10am-8pm CT.
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RAYMOND NIMOX
Primary Examiner
Art Unit 2857
/RAYMOND L NIMOX/Primary Examiner, Art Unit
Prosecution Insights