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 .
Status of Claims
Claims 1-3, 5, 14, 16-17 and 19 are pending and are examined herein.
Claims 14 and 16-17 are rejected under 35 USC 112(b).
Claims 1-3, 5, 14, 16-17 and 19 are rejected under 35 USC 101 as being directed to an abstract idea without significantly more.
Claims 1-3, 5, 14, 16-17 and 19 are rejected under 35 USC 103.
Response to Arguments
Applicant’s arguments filed 01/02/2026 regarding the rejection under 35 USC 101 have been fully considered, but are not persuasive. Applicant argues that “independent claim 1 has been amended to require using machine learning to generate and train a Bayesian network model, which is not an abstract idea because it is directed to a specific machine-implemented approach to generating a specific result.” Examiner respectfully disagrees that the claims are eligible. The recitation of the Bayesian network is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).
Applicant’s arguments filed 01/02/2026 that the amendment overcomes the rejection of claim 1 in view of Yan and Haghighat-Kashani has been fully considered and is persuasive. New grounds of rejection of claim 1 necessitated by amendment are presented herein.
Claim Rejections - 35 USC § 112(b)
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 14 and 16-17 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.
Claim 14 recites “the trained network model”. This limitation lacks proper antecedent basis. For the purposes of examination, “the trained network model” is being interpreted as “the trained Bayesian network model”.
Claim 16 recites “The system of claim 4”; however, claim 4 was canceled. Claim 17 inherits the same issue and is rejected with the same rationale. For the purposes of examination, claim 16 is being interpreted as depending on claim 1.
Claim Rejections - 35 USC § 101 – Abstract Idea
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 1-3, 5, 14, 16-17 and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis
Each of the claims fall within one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Step 2 Analysis
Claim 1 includes the following recitation of an abstract idea:
identify, based on the aggregated energy consumption data, respective percentages of the aggregated energy consumed for respective energy consumption devices of the two or more energy consumption devices. (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.)
Claim 1 recites the following additional elements which, considered individually and as an ordered combination, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
A system comprising: a memory storing instructions; one or more processors configured to execute the instructions to: (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
using machine learning to generate and train a Bayesian network model; (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
receive aggregated energy consumption data indicating total aggregated energy consumed by two or more energy consumption devices over a time interval of at least one day; and (This is insignificant extra-solution activity. See MPEP 2106.05(g). Moreover, sending or receiving data is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data.)
using the Bayesian network model to (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 1 does not reflect an improvement to computer technology or any other technology.
Claim 2 recites at least the abstract idea identified above in the claim upon which it depends.
Claim 2 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
wherein identifying the respective amounts of energy consumed is further based on one or more features associated with the two or more energy consumption sources or an environment thereof. (This is a recitation of using data of a particular type or source to perform the abstract idea. This is an attempt to limit the abstract idea to a particular field of use or technological environment. See MPEP 2106.05(h).)
Claim 2 does not reflect an improvement to computer technology or any other technology.
Claim 3 recites at least the abstract idea identified above in the claim upon which it depends.
Claim 3 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
wherein the one or more features comprise a user feature descriptive of a user associated with the two more energy consumption sources, a premises containing the two or more energy consumption sources, or a combination thereof. (This is a recitation of using data of a particular type or source to perform the abstract idea. This is an attempt to limit the abstract idea to a particular field of use or technological environment. See MPEP 2106.05(h).)
Claim 3 does not reflect an improvement to computer technology or any other technology.
Claim 5 recites at least the abstract idea identified above in the claim upon which it depends, and further recites
utilizes a directed acyclic graph (DAG) to represent probabilistic dependencies between a plurality of features and energy consumption by individual energy consumption sources. (This is practical to perform in the human mind under its broadest reasonable interpretation, perhaps assisted by pen and paper. See for example Figures 5A-B for examples of DAGs which could be created and used by a person using pen and paper. This is a recitation of a mental process.)
Claim 5 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
wherein the Bayesian network model (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 5 does not reflect an improvement to computer technology or any other technology.
Claim 14 recites at least the abstract idea identified above in the claim upon which it depends, and further recites
wherein identifying the respective amounts of energy consumed comprises applying a maximum a posteriori (MAP) estimation process (This is a recitation of a mathematical concept.)
Claim 14 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
using the trained network model. (This is a high level recitation of generic computer components for performing the abstract idea. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
Claim 14 does not reflect an improvement to computer technology or any other technology.
Claim 16 recites at least the abstract idea identified above in the claim upon which it depends.
Claim 16 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
wherein the one or more features comprise at least one external feature independent of specific user actions or properties. (This is a recitation of using data of a particular type or source to perform the abstract idea. This is an attempt to limit the abstract idea to a particular field of use or technological environment. See MPEP 2106.05(h).)
Claim 16 does not reflect an improvement to computer technology or any other technology.
Claim 17 recites at least the abstract idea identified above in the claim upon which it depends.
Claim 17 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
wherein the at least one external feature include at least one of a heating degree day (HDD) metric, a cooling degree day (CDD) metric, a property year-built metric, a building type metric, a locational metric, a climate metric, or a combination thereof. (This is a recitation of using data of a particular type or source to perform the abstract idea. This is an attempt to limit the abstract idea to a particular field of use or technological environment. See MPEP 2106.05(h).)
Claim 17 does not reflect an improvement to computer technology or any other technology.
Claim 19 recites at least the abstract idea identified above in the claim upon which it depends.
Claim 19 recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea:
wherein the set of one or more features includes a housing type, a square-footage, a rent-or-own status, an occupant amount, a refrigerator amount, an air-conditioning characteristic, a heating characteristic, or a combination thereof. (This is a recitation of using data of a particular type or source to perform the abstract idea. This is an attempt to limit the abstract idea to a particular field of use or technological environment. See MPEP 2106.05(h).)
Claim 19 does not reflect an improvement to computer technology or any other technology.
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 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 1-3, 16-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over “Yan” (A simplified energy performance assessment method for existing buildings based on energy bill disaggregation) in view of “Haghighat-Kashani” (US 2014/0336960 A1), further in view of “Tomlinson” (US 2010/0305889 A1).
Regarding claim 1, Yan teaches
receive aggregated energy consumption data indicating total aggregated energy consumed by two or more energy consumption devices over a time interval of at least one day; and (Yan, Figure 1 shows that monthly electricity bill is one of the inputs. Page 564, first full paragraph explains that the monthly bill represents energy values that are aggregated across multiple end users. See Section 2.1, first paragraph for a description of the end-users, which include HVAC, lights, office equipment, lift, mechanical ventilation fans, water heaters, etc.)
...identify, based on the aggregated energy consumption data, respective percentages of the aggregated energy consumed for respective energy consumption devices of the two or more energy consumption devices. (Yan, Figure 1, shows that the energy data is disaggregated. See Section 2.1, first paragraph for a description of the end-users, which include HVAC, lights, office equipment, lift, mechanical ventilation fans, water heaters, etc. Page 570, second paragraph shows the data being reported as a percent.)
Yan does not appear to explicitly teach
A system comprising: a memory storing instructions; one or more processors configured to execute the instructions to
However, Haghighat-Kashani—directed to analogous art--teaches
A system comprising: a memory storing instructions; one or more processors configured to execute the instructions to (Haghighat-Kashani, [0058])
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yan to use a computer as taught by Haghighat-Kashani because using a computer would improve efficiency over performing the calculations by hand.
The combination of Yan and Haghighat-Kashani does not appear to explicitly teach
using machine learning to generate and train a Bayesian network model;...using the Bayesian network model to
However, Tomlinson—directed to analogous art--teaches
using machine learning to generate and train a Bayesian network model;...using the Bayesian network model to (Tomlinson, Abstract describes performing energy disaggregation. [0024-0028] indicates that this is performed using a Bayesian network. The generation/training of the model is described at [0024], e.g., “estimate an appliance probabilistic model...In one embodiment, the APE utilizes a Bayesian Network”. Estimating a model means generating/training the model as generating/training a model means determining or estimating the parameters or structure of the model.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yan and Haghighat-Kashani by Tomlinson because “it reduces computation through cascaded Bayesian network in APE. It further enables close to real-time appliance identification of a household” (Tomlinson, [0030]).
Regarding claim 2, the rejection of claim 1 is incorporated herein. Furthermore, Yan teaches
wherein identifying the respective amounts of energy consumed is further based on one or more features associated with the two or more energy consumption sources or an environment thereof. (Yan, Section 3.1., first paragraph provides an example in which the input data includes manuals of main equipment and key operation records of HVAC systems and also weather data. See also Table 1, “HVAC data” and Monthly electricity bill.)
Regarding claim 3, the rejection of claim 2 is incorporated herein. Furthermore, Yan teaches
wherein the one or more features comprise a user feature descriptive of a user associated with the two more energy consumption sources, a premises containing the two or more energy consumption sources (Yan, page 565, Table 1, Building design data), or a combination thereof.
Regarding claim 16, the rejection of claim 4 is incorporated herein. Furthermore, Yan teaches
wherein the one or more features comprise at least one external feature independent of specific user actions or properties. (Yan, page 564, Table 1, Building design data and weather conditions.)
Regarding claim 17, the rejection of claim 16 is incorporated herein. Furthermore, Yan teaches
wherein the at least one external feature include at least one of a heating degree day (HDD) metric (Yan, page 565, first full paragraph of right hand column), a cooling degree day (CDD) metric (Yan, page 565, first full paragraph of right hand column), a property year-built metric, a building type metric (Yan, page 564Table 1 and Yan, page 568, Table 2: any of the features associated with a building could be interpreted as falling within a “building type” metric. See sections 3.1-3.2 for a more detailed discussion.), a locational metric, a climate metric (Yan, page 564, Table 1 “weather conditions”), or a combination thereof (Yan teaches each of the features identified above.)
Regarding claim 19, the rejection of claim 3 is incorporated herein. Furthermore, Yan teaches
wherein the set of one or more features includes a housing type, a square-footage, a rent-or-own status, an occupant amount (Yan, page 564, “Building design data”, “occupants), a refrigerator amount, an air-conditioning characteristic (Yan, page 564, Table 1 “HVAC data”.), a heating characteristic (Yan, page 564, Table 1, “thermal parameters of building envelopes” and also outdoor temperature and solar radiation are all “heating characteristics”), or a combination thereof (Yan, Table 1).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over “Yan” (A simplified energy performance assessment method for existing buildings based on energy bill disaggregation) in view of “Haghighat-Kashani” (US 2014/0336960 A1), further in view of “Tomlinson” (US 2010/0305889 A1), with “Wikipedia” (Bayesian network, version from 10 April 2014) used to explain the meaning of a term used in the references.
Regarding claim 5, the rejection of claim 4 is incorporated herein. The combination of Yan and Haghighat-Kashani does not appear to explicitly teach
wherein the Bayesian network model utilizes a directed acyclic graph (DAG) to represent probabilistic dependencies between a plurality of features and energy consumption by individual energy consumption sources.
However, Tomlinson teaches
wherein the Bayesian network model utilizes a directed acyclic graph (DAG) to represent probabilistic dependencies between a plurality of features and energy consumption by individual energy consumption sources. (Tomlinson, [0027] explains that the Bayesian network is a directed graphical model that relates features related to energy consumption to energy consumption via the use of probabilistic dependencies. Wikipedia explains what a person of ordinary skill in the art would understand a Bayesian network to include. In particular, the first paragraph explains that the directed graph used in a Bayesian network is acyclic.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined these references in this way for the same reasons given above with respect to claim 4.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over “Yan” (A simplified energy performance assessment method for existing buildings based on energy bill disaggregation) in view of “Haghighat-Kashani” (US 2014/0336960 A1), further in view of “Tomlinson” (US 2010/0305889 A1), and further in view of “G” (US 2013/0231795 A1).
Regarding claim 14, the rejection of claim 1 is incorporated herein. Yan does not appear to explicitly teach
wherein identifying the respective amounts of energy consumed comprises applying a maximum a posteriori (MAP) estimation process using the trained network model.
However, G—directed to analogous art--teaches
wherein identifying the respective amounts of energy consumed comprises applying a maximum a posteriori (MAP) estimation process using the trained network model. (G, Abstract and [0090] describes performing load disaggregation. [0044-0045] indicate that G uses a graphical model and solves the disaggregation problem by computing a maximum a posteriori value.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Yan and Haghighat-Kashani by G because the technqiues of G allow for disaggregation “with good confidence scores and accuracy” (G, [0091]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Markus A Vasquez whose telephone number is (303)297-4432. The examiner can normally be reached Monday to Friday 10AM to 2PM PT.
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/MARKUS A. VASQUEZ/Primary Examiner, Art Unit 2121