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 .
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.
(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.
Claims 9-15 and 21 are rejected under 35 U.S.C. 102(1) as being anticipated by “Analytics for Understanding Customer Behavior in the Energy and Utility Industry”, Kim et al. (hereinafter Kim).
Regarding claim 9, Kim discloses an estimated residential energy efficiency rating mechanism comprising:
an energy rating data storage configured to store a history of energy efficiency ratings for residential home, wherein the history of energy efficiency ratings for residential homes does not include a target residential home (see Fig. 1, Page 11:2, col. 2-Page 11:3, col. 1 for Customer data landscape in the energy and utility industry to equate “the energy efficiency ratings”. This section discloses data associated with residential customers such as attributes, location, house size, type, historical marketing data provides insights on energy efficiency programs, etc.);
wherein the target residential home is a residence for which connected home data is unavailable such that a residential energy efficiency rating for the target residential home cannot be directly calculated (Notes: In light of Applicants’ argument, Remarks at Pages 5 and 6 “As discussed above, with regards to the § 112 rejection, support can be found in [0054]-[0055] of the Specification [0054] states that "[h]aving a calculated residential energy efficiency rating (REER) for a statistically significant number of residences nay allow one to estimate the rating for residences for which no connected home data exist." (emphasis added); [0055] states that "the statistical models most accurately estimate the residential energy efficiency rating for residences for which the residential energy efficiency rating cannot be directly calculated. In other words, according to some embodiments, the application describes using the history of energy efficiency ratings for residential homes to estimate the residential energy efficiency rating (eREER) of a residence "for which the energy efficiency rating cannot be directly calculated" e.g., the claimed target residential home”, in the Examiner’s position, Kim discloses the amended limitation as Kim takes inputs such as structure size, age, and type of construction, to statistical modeling techniques, such as neural networks, regression models, or decision trees, which may be correlated with the rating”, It is the Examiner’s position that Kim meets the newly amended limitation because Kim discloses at Fig. 2: curated data inputs for various models. See Analytics and data process overview section at Pages 3 and 4: once data are consolidate and curated, a series of steps are required to build a model. Once the attributes are processed, a set of machine learning tools are used to evaluate the feasibility of model applications as shown in Fig. 2. Clustering analysis and other unsupervised machine learning techniques are used to identify similar customers (e.g. “target residential”) in a particular context. See Fig. 3 and Page 4 for an analytics flow diagram describing the flow from the curated data, feature extraction, feature derivation, to model training and applications where any model can be built following the steps. Page 4 further discloses techniques such as K-means, two step clustering, Kohonen self-forming map, supervised decision trees, Regression Trees, GLMs, NN, and semi-supervised learning techniques such as regularized support vector machines (R-SVMs) for each category of problems. Further, Kim discloses first, similar detailed and compelling description of customers, which households should have similar energy consumption capture the energy consumption behavior of customers patterns. Second, building characteristics, demographic better. Note that the finer grained clusters give more attributes, and historical energy consumption can be conservative estimate of the energy savings potential, effectively used to predict potential energy savings. Third, which is due to that fact that the reference energy normal energy consumption can be described by consumption in a smaller cluster is closer to the other regression variables such as house size, house age, and customers in each cluster. a model parameters storage connected to the training processor and configured to store the statistical model parameters for the energy efficiency ratings (Fig. 3, Pages 11:4 and 11:5);
a model training processor connected to the energy rating data storage configured to use the history of energy efficiency ratings to calculate statistical model parameters for the energy efficiency ratings according to an appropriate statistical model (Fig. 2: curated data inputs for various models. See Analytics and data process overview section at Pages 3 and 4: once data are consolidate and curated, a series of steps are required to build a model. Once the attributes are processed, a set of machine learning tools are used to evaluate the feasibility of model applications as shown in Fig. 2. Clustering analysis and other unsupervised machine learning techniques are used to identify similar customers in a particular context. See Fig. 3 and Page 4 for an analytics flow diagram describing the flow from the curated data, feature extraction, feature derivation, to model training and applications where any model can be built following the steps. Page 4 further discloses techniques such as K-means, two step clustering, Kohonen self-forming map, supervised decision trees, Regression Trees, GLMs, NN, and semi-supervised learning techniques such as regularized support vector machines (R-SVMs) for each category of problems);
a model parameters storage connected to the model training processor connected to the model training processor and configured to store the statistical model parameters for the energy efficiency ratings (Fig. 3, Pages 11:4 and 11:5);
an estimated REER (eREER) residential energy efficiency rating (eREER) calculation processor connected to the model parameter storage configured to calculate an eREER for the target residential home using the statistical model parameters for the energy efficiency ratings (Page 11:5: Section Outcome; Page 11:6: Approach; and Page 11:7 for Validation of logistic regression model. Also see Table 3 for a summary of all models).
and one or more storages connected to the model training processor and the eREER calculation processor configured to store the estimated residential energy efficiency ratings (see Table 3, Pages 11:6 and 7).
Regarding claim 10, Kim discloses wherein the one more storage are selected from a group comprising a residential structure data storage and a consumer demographic data storage (see Fig. 1 for customer data landscape. Page 2: data associated with residential customers).
Regarding claim 11, Kim discloses an eREER data application program interface (API) connected to the eREER calculation processor (see Pages 6-8).
Regarding claim 12, Kim discloses one or more client applications connected to the eREER data API (Fig. 2).
Regarding claim 13, Kim discloses an eREER storage connected to the eREER calculation processor and to the eREER data API; and one or more client applications connected to the eREER calculation processor and to the eREER storage (Pages 6-8, Table 3).
Regarding claim 14, Kim discloses wherein: a client application requests on-demand results from the eREER calculation processor, where the results are calculated on-the-fly, through the eREER data API; or a client application makes a request for results of the eREER calculation processor that have been calculated previously and saved in the eREER storage (See Page 5: Outcome).
Regarding claim 15, Kim discloses wherein the one or more client applications are connected to the eREER data API via an internet (Fig. 1 configuration).
Regarding claim 21, Kim discloses an estimated residential energy efficiency rating mechanism comprising:
an energy rating data storage configured to store a history of energy efficiency ratings for residential homes, wherein the history of energy efficiency ratings for residential homes does not include a target residential home (Fig. 1, Page 11:2, col. 2-Page 11:3, col. 1 for Customer data landscape in the energy and utility industry to equate “the energy efficiency ratings”. This section discloses data associated with residential customers such as attributes, location, house size, type, historical marketing data provides insights on energy efficiency programs, etc);
a residential structure data storage configured to store information about the residential homes (Figs. 1-3);
a consumer demographic data storage configured to store information about consumers that reside in the residential homes (Figs. 1-3);
a model training processor connected to the energy rating data storage, the residential structure data storage, and the consumer demographic data storage and configured to use the history of energy efficiency ratings, the information about the residential homes, and the information about consumers that reside in the residential homes to calculate statistical model parameters for the energy efficiency ratings according to an appropriate statistical model; (Fig. 2: curated data inputs for various models. See Analytics and data process overview section at Pages 3 and 4: once data are consolidate and curated, a series of steps are required to build a model. Once the attributes are processed, a set of machine learning tools are used to evaluate the feasibility of model applications as shown in Fig. 2. Clustering analysis and other unsupervised machine learning techniques are used to identify similar customers in a particular context. See Fig. 3 and Page 4 for an analytics flow diagram describing the flow from the curated data, feature extraction, feature derivation, to model training and applications where any model can be built following the steps. Page 4 further discloses techniques such as K-means, two step clustering, Kohonen self-forming map, supervised decision trees, Regression Trees, GLMs, NN, and semi-supervised learning techniques such as regularized support vector machines (R-SVMs) for each category of problems” )
wherein the target residential home is a residence for which connected home data is unavailable such that a residential energy efficiency rating for the target residential home cannot be directly calculated (Notes: In light of Applicants’ argument, Remarks at Pages 5 and 6 “As discussed above, with regards to the § 112 rejection, support can be found in [0054]-[0055] of the Specification [0054] states that "[h]aving a calculated residential energy efficiency rating (REER) for a statistically significant number of residences nay allow one to estimate the rating for residences for which no connected home data exist." (emphasis added); [0055] states that "the statistical models most accurately estimate the residential energy efficiency rating for residences for which the residential energy efficiency rating cannot be directly calculated. In other words, according to some embodiments, the application describes using the history of energy efficiency ratings for residential homes to estimate the residential energy efficiency rating (eREER) of a residence "for which the energy efficiency rating cannot be directly calculated" e.g., the claimed target residential home”, in the Examiner’s position, Kim discloses the amended limitation as Kim takes inputs such as structure size, age, and type of construction, to statistical modeling techniques, such as neural networks, regression models, or decision trees, which may be correlated with the rating”, It is the Examiner’s position that Kim meets the newly amended limitation because Kim discloses at Fig. 2: curated data inputs for various models. See Analytics and data process overview section at Pages 3 and 4: once data are consolidate and curated, a series of steps are required to build a model. Once the attributes are processed, a set of machine learning tools are used to evaluate the feasibility of model applications as shown in Fig. 2. Clustering analysis and other unsupervised machine learning techniques are used to identify similar customers (e.g. “target residential”) in a particular context. See Fig. 3 and Page 4 for an analytics flow diagram describing the flow from the curated data, feature extraction, feature derivation, to model training and applications where any model can be built following the steps. Page 4 further discloses techniques such as K-means, two step clustering, Kohonen self-forming map, supervised decision trees, Regression Trees, GLMs, NN, and semi-supervised learning techniques such as regularized support vector machines (R-SVMs) for each category of problems. Further, Kim discloses first, similar detailed and compelling description of customers, which households should have similar energy consumption capture the energy consumption behavior of customers patterns. Second, building characteristics, demographic better. Note that the finer grained clusters give more attributes, and historical energy consumption can be conservative estimate of the energy savings potential, effectively used to predict potential energy savings. Third, which is due to that fact that the reference energy normal energy consumption can be described by consumption in a smaller cluster is closer to the other regression variables such as house size, house age, and customers in each cluster. a model parameters storage connected to the training processor and configured to store the statistical model parameters for the energy efficiency ratings (Fig. 3, Pages 11:4 and 11:5);
an estimated residential energy efficiency rating (eREER) calculation processor connected to the model parameters storage configured to calculate an eREER for the target residential home using the statistical model parameters for the energy efficiency ratings (Page 11:5: Section Outcome; Page 11:6: Approach; and Page 11:7 for Validation of logistic regression model. Also see Table 3 for a summary of all models); wherein the eREER is based at least partially on an HVAC cycle off-time duration; and one or more storages connected to the model training processor and the eREER calculation processor configured to store the estimated residential energy efficiency ratings (see Table 3, Pages 11:6 and 7).
Response to Arguments
Applicant's arguments filed on 07/10/2026 have been fully considered but they are not persuasive.
Prior Art rejection: Applicant argues at Pages 5 and 6 that Kim does not disclose the newly added limitation “wherein the target residential home is a residence for which connected home data is unvailable such that…cannot be directly calculated”. Examiner respectfully disagrees. Please see explanation in claims 9 and 21 in this Office action.
35 USC 112(1), Applicant has removed the limitation “wherein eREER calculation processor calculates the eREER using HVAC cycle time duration”, therefore, the rejection of the claims under 35 USC 112(1) has been withdrawn.
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.
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/PHUONG HUYNH/Primary Examiner, Art Unit 2857 September 11, 2026
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