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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on February 06, 2026 has been entered.
Response to Arguments
Applicant's arguments filed on February 06, 2026 have been fully considered but they are not persuasive.
35 USC 112(2)
Applicant alleges that the limitation, “wherein the eREER calculation processor calculates…duration” is not supported by the orginally filed Specification and that the limitation has been removed. However, the amended claim 9 still recites the limitation. Please see the rejection in this office action.
35 USC 102 and 103
Applicants argue that the current prior art of record does not disclose the amended newly added limitation “wherein the history of energy efficiency ratings for residential homes does not include a target residential home”.
Examiner respectfully disagrees. Please see the rejections of the claims below in this Office Action.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 9-15 and 21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 9, limitation “wherein the eREER calculation processor calculates the eREER using HVAC cycle time duration” is not supported by the originally filed Specification.
Applicant’s argument and response to argument from last office action is herein incorporated.
Applicants argues at Page 5, first full Paragraph, Remarks filed on 08/20/2025, that the amendments can be found, for example, Fig. 2A and its corresponding description of the as-filed Specification.
Examiner finds it non-persuasive because Fig. 2A and its corresponding description, Page 10 only discloses calculating eREER using/based on HVAC cycle off-time duration. Further, item 234 in Fig. 2A recites “calculate off-time cycle for each relevant cycle”.
Please see Page 10 description which is reproduced for Applicants’ convenience:
Figure 2a is a diagram of a flow chart for a REER calculation. At symbol or step 230, customer information, such as residence location, may be loaded from the customer database 26 for the next residence. At step 231, historical device data, such as HVAC equipment run-time, may be loaded from the device data storage 21 for the residence. Typically, a number of days, such as seven, of data may be loaded to stabilize the final rating. At step 232, additional data, such as outdoor weather data, may be loaded from data storage 23, 24, and 25. The data range may match the data retrieved in step 231. At step 233, cycles may be identified in the device data by observing the events that indicate that the HVAC equipment has been turned on. A cycle may be a period between two consecutive events that indicate that the HVAC equipment has been turned on. To minimize external influences, such as solar radiation or activity in the residence, certain periods during the day may be excluded from this analysis. Cycles may also be disregarded if certain temperature ranges are observed, or for other reasons. At step 234, each cycle may consist of a period when the HVAC equipment is continuously on, followed immediately by a period when the HVAC equipment is continuously off. The off-time cycle features may consist of the time that the HVAC equipment is off and the temperature difference between inside and outside the house. At step 235, the previous steps may result in a number of off-time cycle features calculated for each residence. To get to a single number, a representative cycle may be selected. Examples may include taking the mean cycle, or the median cycle, or a mathematical modeling approach that can be used to characterize the relationship between off-times and the in/out temperature differences. At step 236, the calculated off-time model may be scaled to a rating system that is more convenient, such as a star rating. At step 237, all or some of the results may be stored into the REER data storage 28) for later retrieval. At step 338, if not all residences have been processed, continue to the next residence; or otherwise, one may stop.
Therefore, the newly added limitation is not supported by the as-filed Specification.
Further, Applicant argues that Kim is silent regarding leveraging “HVAC cycle time duration”. Examiner find no support for “HVAC cycle time duration” in Applicant’s as-filed Specification, Fig. 2A and its corresponding description, which is the reproduced portion provided above, i.e. Specification, Page 10.
Claim 9 and 21’s newly added limitations, “wherein the history of energy efficiency ratings for residential homes does not include a target residential home” and “an estimated residential energy efficiency (eREER)…configured to calculate an eREER for the target residential home using the statistical model parameters for the energy efficiency ratings” are not support by the Specification. Examiner find no support for the amended limitations.
Claims 10-15 depend from rejected claim 9 and therefore are also rejected.
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.
Claim 21, as best understood, is 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).
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);
a model parameters storage 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 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).
Claim Rejections - 35 USC § 103
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 9-15, as best understood, are rejected under 35 U.S.C. 103 as being unpatentable over Kim and Helbling et al. (USPAP. 20210318014) (hereinafter “Hebling”).
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.);
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).
However, Kim does not explicitly disclose “wherein the eREER calculation processor calculates the eREER using HVAC cycle time duration”.
Helbling teaches “wherein the eREER calculation processor calculates the eREER using HVAC cycle time duration” (Pars. 63, 64: The technical adapter system 200 does this with improved or optimized cycling operation. The adapter device 220 (i.e., the application program component operational therein) calculates specific frequencies of operation of the HVAC system components during heating and cooling cycles. The cycle rate as used herein is the number of times the HVAC system component(s) is powered on and off per hour combined with the total duration the components remain on, within a given cycle. Energy efficiency is enhanced when the HVAC component power cycles exactly match the rates at which temperature changes, indicated, for example, by sensor 138, and the out-door temperature, determined, for example, via a weather channel for a given area at which the HVAC-controlled space is located. ; also see Abstract).
It would have been obvious to one of ordinary skilled in the art at the time of filling the Application to modify Kim's invention using Helbling's invention to arrive at the claimed invention specified in claim 9 to optimize the energy efficiency of the HVAC system components (Helbling: Abstract).
Regarding claim 10, Kim and Helbling disclose everything as applied above. In addition, 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 and Helbling disclose everything as applied above. In addition, Kim discloses an eREER data application program interface (API) connected to the eREER calculation processor (see Pages 6-8).
Regarding claim 12, Kim and Helbling disclose everything as applied above. In addition, Kim discloses one or more client applications connected to the eREER data API (Fig. 2).
Regarding claim 13, Kim and Helbling disclose everything as applied above. In addition, 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 and Helbling disclose everything as applied above. In addition, 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 and Helbling disclose everything as applied above. In addition, Kim discloses wherein the one or more client applications are connected to the eREER data API via an internet (Fig. 1 configuration).
Conclusion
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/PHUONG HUYNH/Primary Examiner, Art Unit 2857 March 7, 2026