DETAILED ACTION
Claims 1-20 have been presented for examination.
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 .
Priority
Acknowledgement is made of applicant’s claim for priority to parent application 17/200,027 filed on 12 March 2021 now issued patent 11651121 B2. Application 17/200,027 is a continuation of 16/708,021 filed on 09 December 2019 now issued patent 10963605 B2. Application 16/708,021 is a continuation of 16/458,502 filed on 01 July 2019 now issued patent 10503847 B2. Application 16/458,502 is a continuation of 14/631,798 filed on 25 February 2015 now issued patent 10339232 B1.
Information Disclosure Statement
The information disclosure statements filed 28 July 2023 fail to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered. NPL references that are missing have been lined through.
Drawings
The drawings received on 09 May 2023 are accepted.
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 § 2146 et seq. 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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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/apply/applying-online/eterminal-disclaimer.
Claims 1, 3-4, 11 and 13-14 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1,4, 6, 10, 12, and 14 of U.S. Patent No. 11,423,199 B1 in view of U.S. Patent Application 2014/0142905 A1. Although the conflicting claims are not identical, they are not patentably distinct from each other because the limitations of the instant claim are found in the parent application as noted above. See table below with bold for emphasis.
Instant Application
U.S. Patent 11,423,199
Claim 1. A system for balance-point-thermal-conductivity-based building analysis with the aid of a digital computer, comprising: a computer comprising a processor configured to execute code stored in a memory, the computer configured to:
obtain a total thermal conductivity of a building;
identify a balance point thermal conductivity of the building based on a balance point up to which the building can be thermally sustained using only internal heating period for a time period;
divide the balance point thermal conductivity by an area of the building to obtain the balance point thermal conductivity per unit of the area;
obtain a further balance point thermal conductivity per the unit of a further area of at least one further building and a further total thermal conductivity of the at least one further building;
and compare the balance point thermal conductivity per unit of the area of the building to the further balance point thermal conductivity per the unit of the further area of the at least one further building and compare the total thermal conductivity to the further total conductivity of the at least one building.
Claim 10. A system for determining a post-modification building balance point temperature with the aid of a digital computer, comprising: a computer comprising a memory storing program code and a processor coupled to the memory and configured to execute the code, the computer configured to:
determine a first balance point temperature of a building; obtain total thermal conductivity of the building; obtain a temperature inside the building; receive a list of one or more changes to a shell of the building associated with a change to the total thermal conductivity; model changed total thermal conductivity associated with an implementation of the building shell changes;
compare the changed total thermal conductivity to the total thermal conductivity, wherein the comparison comprises determining a fraction of the total thermal conductivity that comprises the changed thermal conductivity;
and determine a further balance point temperature of the building associated with the implementation of the one or more changes on the list using a result of the comparison, the temperature inside the building, and the first balance point temperature, wherein the one or more changes on the list are implemented based on the further balance point temperature.
Claim 12. A system according to claim 10, the computer further configured to: obtain energy consumption data associated with the building during a time period and outdoor temperature data during the time period associated with the building, wherein the first balance point temperature is determined using the energy consumption data and the outdoor temperature data.
Claim 3. A system according to Claim 2, wherein the thermal conductivity is obtained using an empirical test conducted using the heating source.
Claim 14. A system according to claim 10, wherein the total thermal conductivity is obtained using at least one empirical test.
Claim 4. A system according to Claim 1, the computer further configured to model at least one change to the building, wherein the change is performed based on the comparison.
Claim 10. receive a list of one or more changes to a shell of the building associated with a change to the total thermal conductivity; model changed total thermal conductivity associated with an implementation of the building shell changes;
Claim 11. A method for balance-point-thermal-conductivity-based building analysis with the aid of a digital computer, comprising:
obtaining by a computer, the computer comprising a processor configured to execute code stored in a memory, a total thermal conductivity of a building;
identifying by the computer a balance point thermal conductivity of the building based on a balance point up to which the building can be thermally sustained using only internal heating period for a time period;
dividing by the computer the balance point thermal conductivity by an area of the building to obtain the balance point thermal conductivity per unit of the area;
obtaining by the computer a further balance point thermal conductivity per the unit of a further area of at least one further building and a further total thermal conductivity of the at least one further building; and comparing by the computer the balance point thermal conductivity per unit of the area of the building to the further balance point thermal conductivity per the unit of the further area of the at least one further building and comparing by the computer the total thermal conductivity to the further total conductivity of the at least one building.
Claim 1. A method for determining a post-modification building balance point temperature with the aid of a digital computer, comprising the steps of:
determining by a computer, the computer comprising a memory storing program code and a processor coupled to the memory and configured to execute the code, a first balance point temperature of a building; obtaining by the computer total thermal conductivity of the building; obtaining by the computer a temperature inside the building; receiving by the computer a list of one or more changes to a shell of the building associated with a change to the total thermal conductivity; modeling by the computer changed total thermal conductivity associated with an implementation of the building shell changes; comparing by the computer the changed total thermal conductivity to the total thermal conductivity; and determining by the computer a further balance point temperature of the building associated with the implementation of the one or more changes on the list using a result of the comparison, the temperature inside the building, and the first balance point temperature, wherein the building is interfaced to a power grid and at least one of a generation of electric power by the power grid and distribution of electric power via the power grid is performed based on the further balance point temperature.
Claim 4. A method according to claim 1, further comprising: obtaining by the computer energy consumption data associated with the building during a time period and outdoor temperature data during the time period associated with the building, wherein the first balance point temperature is determined using the energy consumption data and the outdoor temperature data.
Claim 13. A method according to Claim 12, wherein the thermal conductivity is obtained using an empirical test conducted using the heating source.
Claim 6. A method according to claim 1, wherein the total thermal conductivity is obtained using at least one empirical test.
Claim 14. A method according to Claim 11, further comprising modeling at least one change to the building, wherein the change is performed based on the comparison.
Claim 10. receive a list of one or more changes to a shell of the building associated with a change to the total thermal conductivity; model changed total thermal conductivity associated with an implementation of the building shell changes;
U.S. Patent 11,423,199 B1 does not disclose divide the balance point thermal conductivity by an area of the building to obtain the balance point thermal conductivity per unit of the area;
obtain a further balance point thermal conductivity per the unit of a further area of at least one further building and a further total thermal conductivity of the at least one further building;
compare the balance point thermal conductivity per unit of the area of the building to the further balance point thermal conductivity per the unit of the further area of the at least one further building and compare the total thermal conductivity to the further total conductivity of the at least one building;
and conducted using the heating source.
However, D discloses
divide the balance point thermal conductivity by an area of the building to obtain the balance point thermal conductivity per unit of the area;
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
D [0118] “In some embodiments, HDD and CDD values for a building may be calculated by integrating the difference between the outdoor air temperature of the building and a given temperature over a period of time. In one embodiment, the given temperature may be cooling balance point 510 for the building (e.g., to determine a CDD value) or heating balance point 508 for the building (e.g., to determine an HDD value).
D [0121] “One potential energy use model that takes into account the various model parameters illustrated in illustration 500 is as follows:
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where E is the dependent variable representing the energy use or cost plotted along axis 502 in illustration 500. β0 may be a base energy use, such as base energy load 506. β1 may correspond to cooling slope 514 that, when multiplied by the CDD for a particular time, results in an energy use or cost attributable to cooling the building. Similarly, β2 may correspond to heating slope 512 that, when multiplied by the HDD for a particular time, results in an energy use or cost attributable to heating the building. The value of ε may correspond to the amount of error or noise in the model … In further embodiments, the model may be normalized by dividing the model by the internal area of the building. For example, the model may model the normalized energy use (e.g., measured in kWh/ft2) or normalized energy cost (e.g., measured in $/ft2).
obtain a further balance point thermal conductivity per the unit of a further area of at least one further building and a further total thermal conductivity of the at least one further building;
D [0037] “Buildings 102-106 may be located within the same geographic regions as one another or across different geographic regions. For example, building 102 and building 104 may be located in the same city, while building 106 may be located in a different city. Different levels of granularity may be used to distinguish buildings 102-106 as being located in the same geographic region.”
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
D [0118] “In some embodiments, HDD and CDD values for a building may be calculated by integrating the difference between the outdoor air temperature of the building and a given temperature over a period of time. In one embodiment, the given temperature may be cooling balance point 510 for the building (e.g., to determine a CDD value) or heating balance point 508 for the building (e.g., to determine an HDD value).
compare the balance point thermal conductivity per unit of the area of the building to the further balance point thermal conductivity per the unit of the further area of the at least one further building and compare the total thermal conductivity to the further total conductivity of the at least one building;
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
D [0118] “In some embodiments, HDD and CDD values for a building may be calculated by integrating the difference between the outdoor air temperature of the building and a given temperature over a period of time. In one embodiment, the given temperature may be cooling balance point 510 for the building (e.g., to determine a CDD value) or heating balance point 508 for the building (e.g., to determine an HDD value).
D [0110] “FIG. 4E shows the stored probability density functions for a classification being used to assess the performance of a building. Block 472 illustrates the probability density functions for the intensity values of the buildings in the class (e.g., the probability density functions determined in blocks 466, 468 and stored in memory in block 470). For example, assume that one of the groups of buildings includes data centers located in temperate climates. In such a case, probability density functions may exist from the GMMs for intensity values INAC, I--β0, Iβ1, and Iβ2. Using these functions, any number of different comparisons may be made among buildings in the class. In one example shown in block 474, best or worst in class buildings may be identified. In another example shown in block 476, a building under study may be compared to other buildings in its class.”
and conducted using the heating source
D [0050]” Building data 206 may include data from a building's control system, such as set point data (e.g., temperature set points, energy use set points, etc.), control variables or parameters, and calculated metrics from the building's control system.”
11,423,199 B1 and D are analogous to the claimed invention as they both pertain to the analysis of thermal energy usage of a building.
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the teachings of D with U.S Patent 11,423,199 B1 because the energy use model of D would reduce the cost of performing energy analysis compared to approaches that rely heavily on sensor data from a building, and the model can take a wide range of inputs from weather data, billing data, or building data (See D [0039-0040]).
Claims 1, 4, 8-10, 11, 14, and 18-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1,4, 7, 8 of U.S. Patent No. 10,339,232 B1 in view of U.S. Patent Application 2014/0142905 A1. Although the conflicting claims are not identical, they are not patentably distinct from each other because the limitations of the instant claim are found in the parent application as noted above. See table below with bold for emphasis.
Instant Application
U.S. Patent 10,339,232 B1
Claim 1. A system for balance-point-thermal-conductivity-based building analysis with the aid of a digital computer, comprising: a computer comprising a processor configured to execute code stored in a memory, the computer configured to:
obtain a total thermal conductivity of a building;
identify a balance point thermal conductivity of the building based on a balance point up to which the building can be thermally sustained using only internal heating period for a time period;
divide the balance point thermal conductivity by an area of the building to obtain the balance point thermal conductivity per unit of the area;
obtain a further balance point thermal conductivity per the unit of a further area of at least one further building and a further total thermal conductivity of the at least one further building;
and compare the balance point thermal conductivity per unit of the area of the building to the further balance point thermal conductivity per the unit of the further area of the at least one further building and compare the total thermal conductivity to the further total conductivity of the at least one building.
Claim 1. A method for modeling periodic building heating energy consumption with the aid of a digital computer, comprising the steps of:
obtaining total thermal conductivity of a building with an HVAC system;
evaluating thermal conductivity of a building based on a balance point up to which the building can be thermally sustained using only internal heating gains over a time period for a heating season;
and determining fuel consumption as a function of the total thermal conductivity less the balance point thermal conductivity, the average indoor temperature less the average outdoor temperature and duration of the time period, over the HVAC system's efficiency; and modeling a change in the thermal conductivity associated with replacing a material of a portion of an envelope of the building with a further material using an R-value of the material and an R-value of the further material; and calculating a value of energy savings associated with the replacement using the determined fuel consumption and the changed thermal conductivity, wherein the steps are performed on a suitably-programmed computer and wherein the replacement is performed based on the value of the energy savings.
Claim 4. A system according to Claim 1, the computer further configured to model at least one change to the building, wherein the change is performed based on the comparison.
Claim 1. and modeling a change in the thermal conductivity associated with replacing a material of a portion of an envelope of the building with a further material using an R-value of the material and an R-value of the further material;
Claim 8. A system according to Claim 1, the computer further configured to:
identify a temperature difference between an average temperature outside and an average temperature inside the building over a time period for the time period;
identify internal heating gains within the building over the time period;
and find the balance point thermal conductivity as a function of the internal heating gains over the temperature difference and duration of the heating time period.
Claim 7. A method for determining heating energy consumption of a building over a fixed time period with the aid of a digital computer, comprising the steps of:
obtaining total thermal conductivity of a building and an efficiency for an HVAC system that provides the heating to the building;
identifying a temperature difference between an average temperature outside and an average temperature inside the building over a time period for a heating season;
identifying internal heating gains within the building over the time period;
finding balance point thermal conductivity as a function of the internal heating gains over the temperature difference and duration of the time period;
and determining fuel consumption for heating over the time period as a function of the difference between the total thermal conductivity and the balance point thermal conductivity, the temperature difference and the duration of the time period, over the efficiency of the HVAC system, modeling a change in the thermal conductivity associated with replacing a material of a portion of an envelope of the building with a further material using an R-value of the material and an R-value of the further material; and calculating a value of energy savings associated with the replacement using the determined fuel consumption and the changed thermal conductivity, wherein the steps are performed on a suitably-programmed computer and wherein the replacement is performed based on the value of the energy savings.
Claim 9. A system according to Claim 1, wherein
the internal heating gains are identified using occupant heating gains for the building,
heating gains produced by operation of electric devices in the building,
and solar heating gains for the building.
Claim 8. A method according to claim 7, the step of finding balance point thermal conductivity further comprising one or more of the steps of:
finding occupant thermal conductivity as a function of an average number of occupants inside the building during the time period, the temperature difference and the duration of the time period;
finding electric thermal conductivity as a function of electricity consumption of electric devices that deliver all heat that is generated into the interior of the building and in operation in the building during the time period, the temperature difference and the duration of the time period;
and finding solar thermal conductivity as a function of solar energy that entered the building during the time period, the temperature difference and the duration of the time period.
Claim 10. A system according to Claim 1,
wherein the total thermal conductivity comprises the balance point thermal conductivity and thermal conductivity for auxiliary heating.
Claim 4. A method according to claim 3, further comprising the steps of: finding auxiliary heating thermal conductivity as a function of the auxiliary heating gains over the average indoor temperature less the average outdoor temperature and duration of the time period;
and determining the total thermal conductivity of the building as a function of the balance point thermal conductivity and the auxiliary heating thermal conductivity.
Claim 11. A method for balance-point-thermal-conductivity-based building analysis with the aid of a digital computer, comprising: obtaining by a computer, the computer comprising a processor configured to execute code stored in a memory,
a total thermal conductivity of a building;
identify a balance point thermal conductivity of the building based on a balance point up to which the building can be thermally sustained using only internal heating period for a time period;
divide the balance point thermal conductivity by an area of the building to obtain the balance point thermal conductivity per unit of the area;
obtain a further balance point thermal conductivity per the unit of a further area of at least one further building and a further total thermal conductivity of the at least one further building;
and compare the balance point thermal conductivity per unit of the area of the building to the further balance point thermal conductivity per the unit of the further area of the at least one further building and compare the total thermal conductivity to the further total conductivity of the at least one building.
Claim 1. A method for modeling periodic building heating energy consumption with the aid of a digital computer, comprising the steps of:
obtaining total thermal conductivity of a building with an HVAC system;
evaluating thermal conductivity of a building based on a balance point up to which the building can be thermally sustained using only internal heating gains over a time period for a heating season;
and determining fuel consumption as a function of the total thermal conductivity less the balance point thermal conductivity, the average indoor temperature less the average outdoor temperature and duration of the time period, over the HVAC system's efficiency; and modeling a change in the thermal conductivity associated with replacing a material of a portion of an envelope of the building with a further material using an R-value of the material and an R-value of the further material; and calculating a value of energy savings associated with the replacement using the determined fuel consumption and the changed thermal conductivity, wherein the steps are performed on a suitably-programmed computer and wherein the replacement is performed based on the value of the energy savings.
Claim 14. A method according to Claim 11, further comprising modeling at least one change to the building, wherein the change is performed based on the comparison.
Claim 1. and modeling a change in the thermal conductivity associated with replacing a material of a portion of an envelope of the building with a further material using an R-value of the material and an R-value of the further material;
Claim 18. A method according to Claim 11, further comprising:
identify a temperature difference between an average temperature outside and an average temperature inside the building over a time period for the time period;
identify internal heating gains within the building over the time period;
and find the balance point thermal conductivity as a function of the internal heating gains over the temperature difference and duration of the heating time period.
Claim 7. A method for determining heating energy consumption of a building over a fixed time period with the aid of a digital computer, comprising the steps of:
obtaining total thermal conductivity of a building and an efficiency for an HVAC system that provides the heating to the building;
identifying a temperature difference between an average temperature outside and an average temperature inside the building over a time period for a heating season;
identifying internal heating gains within the building over the time period;
finding balance point thermal conductivity as a function of the internal heating gains over the temperature difference and duration of the time period;
and determining fuel consumption for heating over the time period as a function of the difference between the total thermal conductivity and the balance point thermal conductivity, the temperature difference and the duration of the time period, over the efficiency of the HVAC system, modeling a change in the thermal conductivity associated with replacing a material of a portion of an envelope of the building with a further material using an R-value of the material and an R-value of the further material; and calculating a value of energy savings associated with the replacement using the determined fuel consumption and the changed thermal conductivity, wherein the steps are performed on a suitably-programmed computer and wherein the replacement is performed based on the value of the energy savings.
Claim 19. A method according to Claim 11, wherein
the internal heating gains are identified using occupant heating gains for the building,
heating gains produced by operation of electric devices in the building,
and solar heating gains for the building.
Claim 8. A method according to claim 7, the step of finding balance point thermal conductivity further comprising one or more of the steps of:
finding occupant thermal conductivity as a function of an average number of occupants inside the building during the time period, the temperature difference and the duration of the time period;
finding electric thermal conductivity as a function of electricity consumption of electric devices that deliver all heat that is generated into the interior of the building and in operation in the building during the time period, the temperature difference and the duration of the time period;
and finding solar thermal conductivity as a function of solar energy that entered the building during the time period, the temperature difference and the duration of the time period.
Claim 20. A method according to Claim 11,
wherein the total thermal conductivity comprises the balance point thermal conductivity and thermal conductivity for auxiliary heating.
Claim 4. A method according to claim 3, further comprising the steps of: finding auxiliary heating thermal conductivity as a function of the auxiliary heating gains over the average indoor temperature less the average outdoor temperature and duration of the time period;
and determining the total thermal conductivity of the building as a function of the balance point thermal conductivity and the auxiliary heating thermal conductivity.
U.S. Patent 10,339,232 B1 does not disclose divide the balance point thermal conductivity by an area of the building to obtain the balance point thermal conductivity per unit of the area; obtain a further balance point thermal conductivity per the unit of a further area of at least one further building and a further total thermal conductivity of the at least one further building; and compare the balance point thermal conductivity per unit of the area of the building to the further balance point thermal conductivity per the unit of the further area of the at least one further building and compare the total thermal conductivity to the further total conductivity of the at least one building.
However, D discloses
divide the balance point thermal conductivity by an area of the building to obtain the balance point thermal conductivity per unit of the area;
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
D [0118] “In some embodiments, HDD and CDD values for a building may be calculated by integrating the difference between the outdoor air temperature of the building and a given temperature over a period of time. In one embodiment, the given temperature may be cooling balance point 510 for the building (e.g., to determine a CDD value) or heating balance point 508 for the building (e.g., to determine an HDD value).
D [0121] “One potential energy use model that takes into account the various model parameters illustrated in illustration 500 is as follows:
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where E is the dependent variable representing the energy use or cost plotted along axis 502 in illustration 500. β0 may be a base energy use, such as base energy load 506. β1 may correspond to cooling slope 514 that, when multiplied by the CDD for a particular time, results in an energy use or cost attributable to cooling the building. Similarly, β2 may correspond to heating slope 512 that, when multiplied by the HDD for a particular time, results in an energy use or cost attributable to heating the building. The value of ε may correspond to the amount of error or noise in the model … In further embodiments, the model may be normalized by dividing the model by the internal area of the building. For example, the model may model the normalized energy use (e.g., measured in kWh/ft2) or normalized energy cost (e.g., measured in $/ft2).
obtain a further balance point thermal conductivity per the unit of a further area of at least one further building and a further total thermal conductivity of the at least one further building;
D [0037] “Buildings 102-106 may be located within the same geographic regions as one another or across different geographic regions. For example, building 102 and building 104 may be located in the same city, while building 106 may be located in a different city. Different levels of granularity may be used to distinguish buildings 102-106 as being located in the same geographic region.”
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
D [0118] “In some embodiments, HDD and CDD values for a building may be calculated by integrating the difference between the outdoor air temperature of the building and a given temperature over a period of time. In one embodiment, the given temperature may be cooling balance point 510 for the building (e.g., to determine a CDD value) or heating balance point 508 for the building (e.g., to determine an HDD value).
and compare the balance point thermal conductivity per unit of the area of the building to the further balance point thermal conductivity per the unit of the further area of the at least one further building and compare the total thermal conductivity to the further total conductivity of the at least one building.
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
D [0118] “In some embodiments, HDD and CDD values for a building may be calculated by integrating the difference between the outdoor air temperature of the building and a given temperature over a period of time. In one embodiment, the given temperature may be cooling balance point 510 for the building (e.g., to determine a CDD value) or heating balance point 508 for the building (e.g., to determine an HDD value).
D [0110] “FIG. 4E shows the stored probability density functions for a classification being used to assess the performance of a building. Block 472 illustrates the probability density functions for the intensity values of the buildings in the class (e.g., the probability density functions determined in blocks 466, 468 and stored in memory in block 470). For example, assume that one of the groups of buildings includes data centers located in temperate climates. In such a case, probability density functions may exist from the GMMs for intensity values INAC, I--β0, Iβ1, and Iβ2. Using these functions, any number of different comparisons may be made among buildings in the class. In one example shown in block 474, best or worst in class buildings may be identified. In another example shown in block 476, a building under study may be compared to other buildings in its class.”
U.S. Patent 10,339,232 B1 and D are analogous to the claimed invention as they both pertain to the analysis of thermal energy usage of a building.
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the teachings of D with U.S Patent 10,339,232 B1 because the energy use model of D would reduce the cost of performing energy analysis compared to approaches that rely heavily on sensor data from a building, and the model can take a wide range of inputs from weather data, billing data, or building data (See D [0039-0040]).
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.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e. abstract idea) without significantly more.
Step 1: Claims 1-10 are directed to a system, which is a machine, which is a statutory category of invention. Claims 11-20 are directed to a method, which is a process, which is a statutory category of invention. Therefore, claims 1-20 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claims 1 and 11 recite the abstract idea of analyzing balance-point-thermal-conductivity-based buildings, constituting an abstract idea based on Mathematical Concepts including mathematical formulas or equations as well as calculations or alternatively Mental Processes based on concepts performed in the human mind, or with the aid of pencil and paper. The limitations of “identify a balance point thermal conductivity of the building based on a balance point up to which the building can be thermally sustained using only internal heating period for a time period” in claim 1 and “identifying by the computer a balance point thermal conductivity of the building based on a balance point up to which the building can be thermally sustained using only internal heating period for a time period” in claim 11 cover mathematical concepts including applying the obtained total thermal conductivity to mathematical equations to get the balance point thermal conductivity of buildings for a time period. Alternatively, these limitations cover mental processes including applying the obtained total thermal conductivity to mathematical equations to get the balance point thermal conductivity of buildings for a time period, which can be performed with the use of a pencil and paper. Additionally, the limitations of “divide the balance point thermal conductivity by an area of the building to obtain the balance point thermal conductivity per unit of the area” in claim 1 and “dividing by the computer the balance point thermal conductivity by an area of the building to obtain the balance point thermal conductivity per unit of the area” in claim 11 cover mathematical concepts including performing mathematical equations to produce the balance point thermal conductivity per unit of the area. Alternatively, these limitations cover mental processes including performing mathematical equations to produce the balance point thermal conductivity per unit of the area, which can be performed with the use of a pencil and paper. Additionally, the limitations of “compare the balance point thermal conductivity per unit of the area of the building to the further balance point thermal conductivity per the unit of the further area of the at least one further building and compare the total thermal conductivity to the further total conductivity of the at least one building” in claim 1 and “comparing by the computer the balance point thermal conductivity per unit of the area of the building to the further balance point thermal conductivity per the unit of the further area of the at least one further building and compare the total thermal conductivity to the further total conductivity of the at least one building” in claim 11 cover mental processes including taking the balance points thermal conductivity per unit of the area and total thermal conductivity of the initial building, and performing a side by side analysis between the initial building and the at least one further building. Thus, the claims recite the abstract idea of a mental process performed in the human mind, or with the aid of pencil and paper. That is, other than reciting “with the aid of a digital computer” and “by the computer,” nothing in the claim element precludes the step from practically being performed in the mind.
Dependent claims 2-10 and 12-20 further narrow the abstract ideas, identified in the independent claims.
Step 2A, Prong 2: The judicial exception is not integrated into a practical application. In Claims 1 and 11, the additional element of “a computer comprising a processor configured to execute code stored in a memory” merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitations of “obtain a total thermal conductivity of a building” and “obtain a further balance point thermal conductivity per the unit of a further area of at least one further building and a further total thermal conductivity of the at least one further building” in claim 1 and “obtaining, by a computer… a total thermal conductivity of a building” and “obtaining by the computer a further balance point thermal conductivity per the unit of a further area of at least one further building and a further total thermal conductivity of the at least one further building” in claim 11 can be viewed as is insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and does not amount to significantly more. This is akin to selecting information, based on types of information and availability of information in an outdoor temperature environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the judicial exception is not integrated into a practical application.
Dependent claims 2-10 and 12-20 further narrow the abstract ideas, identified in the independent claims, and do not introduce further additional elements for consideration beyond those addressed above.
Step 2B: Claims 1 and 11 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. In Claims 1 and 11, the additional element of “a computer comprising a processor configured to execute code stored in a memory” merely uses a computer device as a tool to perform the abstract idea. (MPEP 2106.05(f)) The limitations of “obtain a total thermal conductivity of a building” and “obtain a further balance point thermal conductivity per the unit of a further area of at least one further building and a further total thermal conductivity of the at least one further building” in claim 1 and “obtaining, by a computer… a total thermal conductivity of a building” and “obtaining by the computer a further balance point thermal conductivity per the unit of a further area of at least one further building and a further total thermal conductivity of the at least one further building” in claim 11 can be viewed as is insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and does not amount to significantly more. This is akin to selecting information, based on types of information and availability of information in an outdoor temperature environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the claim as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception. As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claims 2 and 12 are directed to further defining the computer, which further narrows the abstract idea identified in the independent claim, which is directed to “Mere Instructions to Apply an Exception (MPEP 2106.05(f))” or alternatively “Well-Understood, Routine, and Conventional.”
Dependent claims 3 and 13 are directed to further defining the method of obtaining the thermal conductivity, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes” or alternatively “Mathematical Concepts.”
Dependent claims 4 and 14 and directed to further defining the computer to update at least one change to the building, which further narrows the abstract idea identified in the independent claim, which is directed to “Mere Instructions to Apply an Exception (MPEP 2106.05(f)).”
Dependent claims 5 and 15 are directed to further defining the units of measurement, which further narrows the abstract idea identified in the independent claim, which is directed to “Mental Processes” or alternatively “Mathematical Concepts.”
Dependent claims 6-7 and 16-17 are directed to further defining the at least one further building, which is directed to “Mental Processes.”
Dependent claims 8 and 18 are directed to further obtain the balance point thermal conductivity based on temperature differences and internal heating gains, which is directed to “Mental Processes” or alternatively “Mathematical Concepts.”
Dependent claims 9 and 19 are directed to further define the identification of the internal heating gains, which is directed to “Mental Processes.”
Dependent claims 10 and 20 are directed to further defining the total thermal conductivity, which is directed to “Mental Processes” or alternatively “Mathematical Concepts.”
Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
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 1-2, 5-8, 11-12, and 15-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application 2014/0142905 A1, hereafter D.
Regarding Claim 1: D discloses a system for balance-point-thermal-conductivity-based building analysis with the aid of a digital computer,
comprising: a computer comprising a processor configured to execute code stored in a memory, the computer configured to:
D [0183] “The term "client or "server" include all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones, or combinations, of the foregoing. The apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). The apparatus can also include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them. The apparatus and execution environment can realize various different computing model infrastructures, such as web services, distributed computing and grid computing infrastructures.”
obtain a total thermal conductivity of a building;
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
identify a balance point thermal conductivity of the building based on a balance point up to which the building can be thermally sustained using only internal heating period for a time period
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
D [0118] “In some embodiments, HDD and CDD values for a building may be calculated by integrating the difference between the outdoor air temperature of the building and a given temperature over a period of time. In one embodiment, the given temperature may be cooling balance point 510 for the building (e.g., to determine a CDD value) or heating balance point 508 for the building (e.g., to determine an HDD value).
divide the balance point thermal conductivity by an area of the building to obtain the balance point thermal conductivity per unit of the area
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
D [0118] “In some embodiments, HDD and CDD values for a building may be calculated by integrating the difference between the outdoor air temperature of the building and a given temperature over a period of time. In one embodiment, the given temperature may be cooling balance point 510 for the building (e.g., to determine a CDD value) or heating balance point 508 for the building (e.g., to determine an HDD value).
D [0121] “One potential energy use model that takes into account the various model parameters illustrated in illustration 500 is as follows:
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where E is the dependent variable representing the energy use or cost plotted along axis 502 in illustration 500. β0 may be a base energy use, such as base energy load 506. β1 may correspond to cooling slope 514 that, when multiplied by the CDD for a particular time, results in an energy use or cost attributable to cooling the building. Similarly, β2 may correspond to heating slope 512 that, when multiplied by the HDD for a particular time, results in an energy use or cost attributable to heating the building. The value of ε may correspond to the amount of error or noise in the model … In further embodiments, the model may be normalized by dividing the model by the internal area of the building. For example, the model may model the normalized energy use (e.g., measured in kWh/ft2) or normalized energy cost (e.g., measured in $/ft2). Examiner notes that energy usage of the building model, including the heating and cooling slopes can be divided by the internal area of the building in D[0121], hence the normalized energy use is measured in kWh/ft2.
obtain a further balance point thermal conductivity per the unit of a further area of at least one further building and a further total thermal conductivity of the at least one further building;
D [0037] “Buildings 102-106 may be located within the same geographic regions as one another or across different geographic regions. For example, building 102 and building 104 may be located in the same city, while building 106 may be located in a different city. Different levels of granularity may be used to distinguish buildings 102-106 as being located in the same geographic region.”
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
D [0118] “In some embodiments, HDD and CDD values for a building may be calculated by integrating the difference between the outdoor air temperature of the building and a given temperature over a period of time. In one embodiment, the given temperature may be cooling balance point 510 for the building (e.g., to determine a CDD value) or heating balance point 508 for the building (e.g., to determine an HDD value).
and compare the balance point thermal conductivity per unit of the area of the building to the further balance point thermal conductivity per the unit of the further area of the at least one further building and compare the total thermal conductivity to the further total conductivity of the at least one building.
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
D [0118] “In some embodiments, HDD and CDD values for a building may be calculated by integrating the difference between the outdoor air temperature of the building and a given temperature over a period of time. In one embodiment, the given temperature may be cooling balance point 510 for the building (e.g., to determine a CDD value) or heating balance point 508 for the building (e.g., to determine an HDD value).
D [0110] “FIG. 4E shows the stored probability density functions for a classification being used to assess the performance of a building. Block 472 illustrates the probability density functions for the intensity values of the buildings in the class (e.g., the probability density functions determined in blocks 466, 468 and stored in memory in block 470). For example, assume that one of the groups of buildings includes data centers located in temperate climates. In such a case, probability density functions may exist from the GMMs for intensity values INAC, I--β0, Iβ1, and Iβ2. Using these functions, any number of different comparisons may be made among buildings in the class. In one example shown in block 474, best or worst in class buildings may be identified. In another example shown in block 476, a building under study may be compared to other buildings in its class.”
Regarding Claim 2: D discloses a system according to Claim 1, the computer further configured to remotely control a heating source inside the building to obtain the thermal conductivity.
D [0050]” Building data 206 may include data from a building's control system, such as set point data (e.g., temperature set points, energy use set points, etc.), control variables or parameters, and calculated metrics from the building's control system.”
Regarding Claim 5: D discloses a system according to Claim 1, wherein the units are square feet.
D [0121] “One potential energy use model that takes into account the various model parameters illustrated in illustration 500 is as follows:
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where E is the dependent variable representing the energy use or cost plotted along axis 502 in illustration 500. β0 may be a base energy use, such as base energy load 506. β1 may correspond to cooling slope 514 that, when multiplied by the CDD for a particular time, results in an energy use or cost attributable to cooling the building. Similarly, β2 may correspond to heating slope 512 that, when multiplied by the HDD for a particular time, results in an energy use or cost attributable to heating the building. The value of ε may correspond to the amount of error or noise in the model … In further embodiments, the model may be normalized by dividing the model by the internal area of the building. For example, the model may model the normalized energy use (e.g., measured in kWh/ft2) or normalized energy cost (e.g., measured in $/ft2).
Regarding Claim 6: D discloses a system according to Claim 1, wherein the at least one further building is neighboring the building.
D [0037] “Buildings 102-106 may be located within the same geographic regions as one another or across different geographic regions. For example, building 102 and building 104 may be located in the same city, while building 106 may be located in a different city. Different levels of granularity may be used to distinguish buildings 102-106 as being located in the same geographic region. For example, geographic regions may be divided by country, state, city, metropolitan area, time zone, zip code, area code, latitude, longitude, growing zone, combinations thereof, or using any other geographic classification system.
Regarding Claim 7: D discloses a system according to Claim 1, wherein the at least one further building is in a same city as the building.
D [0037] “Buildings 102-106 may be located within the same geographic regions as one another or across different geographic regions. For example, building 102 and building 104 may be located in the same city, while building 106 may be located in a different city. Different levels of granularity may be used to distinguish buildings 102-106 as being located in the same geographic region. For example, geographic regions may be divided by country, state, city, metropolitan area, time zone, zip code, area code, latitude, longitude, growing zone, combinations thereof, or using any other geographic classification system.
Regarding Claim 8: D discloses a system according to Claim 1, the computer further configured to: identify a temperature difference between an average temperature outside and an average temperature inside the building over a time period for the time period; identify internal heating gains within the building over the time period; and find the balance point thermal conductivity as a function of the internal heating gains over the temperature difference and duration of the heating time period.
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
D [0118] “In some embodiments, HDD and CDD values for a building may be calculated by integrating the difference between the outdoor air temperature of the building and a given temperature over a period of time. In one embodiment, the given temperature may be cooling balance point 510 for the building (e.g., to determine a CDD value) or heating balance point 508 for the building (e.g., to determine an HDD value).
Regarding Claim 11: D discloses a method for balance-point-thermal-conductivity-based building analysis with the aid of a digital computer,
comprising: a computer comprising a processor configured to execute code stored in a memory, the computer configured to:
D [0183] “The term "client or "server" include all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones, or combinations, of the foregoing. The apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). The apparatus can also include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them. The apparatus and execution environment can realize various different computing model infrastructures, such as web services, distributed computing and grid computing infrastructures.”
obtaining a total thermal conductivity of a building;
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
identifying a balance point thermal conductivity of the building based on a balance point up to which the building can be thermally sustained using only internal heating period for a time period
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
D [0118] “In some embodiments, HDD and CDD values for a building may be calculated by integrating the difference between the outdoor air temperature of the building and a given temperature over a period of time. In one embodiment, the given temperature may be cooling balance point 510 for the building (e.g., to determine a CDD value) or heating balance point 508 for the building (e.g., to determine an HDD value).
dividing the balance point thermal conductivity by an area of the building to obtain the balance point thermal conductivity per unit of the area
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
D [0118] “In some embodiments, HDD and CDD values for a building may be calculated by integrating the difference between the outdoor air temperature of the building and a given temperature over a period of time. In one embodiment, the given temperature may be cooling balance point 510 for the building (e.g., to determine a CDD value) or heating balance point 508 for the building (e.g., to determine an HDD value).
D [0121] “One potential energy use model that takes into account the various model parameters illustrated in illustration 500 is as follows:
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where E is the dependent variable representing the energy use or cost plotted along axis 502 in illustration 500. β0 may be a base energy use, such as base energy load 506. β1 may correspond to cooling slope 514 that, when multiplied by the CDD for a particular time, results in an energy use or cost attributable to cooling the building. Similarly, β2 may correspond to heating slope 512 that, when multiplied by the HDD for a particular time, results in an energy use or cost attributable to heating the building. The value of ε may correspond to the amount of error or noise in the model … In further embodiments, the model may be normalized by dividing the model by the internal area of the building. For example, the model may model the normalized energy use (e.g., measured in kWh/ft2) or normalized energy cost (e.g., measured in $/ft2).
obtaining a further balance point thermal conductivity per the unit of a further area of at least one further building and a further total thermal conductivity of the at least one further building;
D [0037] “Buildings 102-106 may be located within the same geographic regions as one another or across different geographic regions. For example, building 102 and building 104 may be located in the same city, while building 106 may be located in a different city. Different levels of granularity may be used to distinguish buildings 102-106 as being located in the same geographic region.”
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
D [0118] “In some embodiments, HDD and CDD values for a building may be calculated by integrating the difference between the outdoor air temperature of the building and a given temperature over a period of time. In one embodiment, the given temperature may be cooling balance point 510 for the building (e.g., to determine a CDD value) or heating balance point 508 for the building (e.g., to determine an HDD value).
and comparing the balance point thermal conductivity per unit of the area of the building to the further balance point thermal conductivity per the unit of the further area of the at least one further building and compare the total thermal conductivity to the further total conductivity of the at least one building.
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
D [0118] “In some embodiments, HDD and CDD values for a building may be calculated by integrating the difference between the outdoor air temperature of the building and a given temperature over a period of time. In one embodiment, the given temperature may be cooling balance point 510 for the building (e.g., to determine a CDD value) or heating balance point 508 for the building (e.g., to determine an HDD value).
D [0110] “FIG. 4E shows the stored probability density functions for a classification being used to assess the performance of a building. Block 472 illustrates the probability density functions for the intensity values of the buildings in the class (e.g., the probability density functions determined in blocks 466, 468 and stored in memory in block 470). For example, assume that one of the groups of buildings includes data centers located in temperate climates. In such a case, probability density functions may exist from the GMMs for intensity values INAC, I--β0, Iβ1, and Iβ2. Using these functions, any number of different comparisons may be made among buildings in the class. In one example shown in block 474, best or worst in class buildings may be identified. In another example shown in block 476, a building under study may be compared to other buildings in its class.”
Regarding Claim 12: D discloses a method according to Claim 11, further comprising remotely controlling a heating source inside the building to obtain the thermal conductivity.
D [0050]” Building data 206 may include data from a building's control system, such as set point data (e.g., temperature set points, energy use set points, etc.), control variables or parameters, and calculated metrics from the building's control system.”
Regarding Claim 15: D discloses a method according to Claim 11, wherein the units are square feet.
D [0121] “One potential energy use model that takes into account the various model parameters illustrated in illustration 500 is as follows:
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where E is the dependent variable representing the energy use or cost plotted along axis 502 in illustration 500. β0 may be a base energy use, such as base energy load 506. β1 may correspond to cooling slope 514 that, when multiplied by the CDD for a particular time, results in an energy use or cost attributable to cooling the building. Similarly, β2 may correspond to heating slope 512 that, when multiplied by the HDD for a particular time, results in an energy use or cost attributable to heating the building. The value of ε may correspond to the amount of error or noise in the model … In further embodiments, the model may be normalized by dividing the model by the internal area of the building. For example, the model may model the normalized energy use (e.g., measured in kWh/ft2) or normalized energy cost (e.g., measured in $/ft2).
Regarding Claim 16: D discloses a method according to Claim 11, wherein the at least one further building is neighboring the building.
D [0037] “Buildings 102-106 may be located within the same geographic regions as one another or across different geographic regions. For example, building 102 and building 104 may be located in the same city, while building 106 may be located in a different city. Different levels of granularity may be used to distinguish buildings 102-106 as being located in the same geographic region. For example, geographic regions may be divided by country, state, city, metropolitan area, time zone, zip code, area code, latitude, longitude, growing zone, combinations thereof, or using any other geographic classification system.
Regarding Claim 17: D discloses a method according to Claim 11, wherein the at least one further building is in a same city as the building.
D [0037] “Buildings 102-106 may be located within the same geographic regions as one another or across different geographic regions. For example, building 102 and building 104 may be located in the same city, while building 106 may be located in a different city. Different levels of granularity may be used to distinguish buildings 102-106 as being located in the same geographic region. For example, geographic regions may be divided by country, state, city, metropolitan area, time zone, zip code, area code, latitude, longitude, growing zone, combinations thereof, or using any other geographic classification system.
Regarding Claim 18: D discloses a method according to Claim 11, further comprising: identify a temperature difference between an average temperature outside and an average temperature inside the building over a time period for the time period; identify internal heating gains within the building over the time period; and find the balance point thermal conductivity as a function of the internal heating gains over the temperature difference and duration of the heating time period.
D [0081] “In general, building statistics may correspond to any value derived from the building's energy use model. For example, a building statistic may be a normalized annual consumption intensity (INAC) value or an intensity value for one of the model’s βi parameters, if a regression model is used to model the building's energy use. Further exemplary building statistics that may be derived from a building's energy use model may include, but are not limited to, the building's thermal efficiency (η), the building's thermal conductance area product (UA), temperature set point (Tsetpt), outdoor air flow (FlowOA), or other such values.”
D [0118] “In some embodiments, HDD and CDD values for a building may be calculated by integrating the difference between the outdoor air temperature of the building and a given temperature over a period of time. In one embodiment, the given temperature may be cooling balance point 510 for the building (e.g., to determine a CDD value) or heating balance point 508 for the building (e.g., to determine an HDD value).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application 2014/0142905 A1, hereafter D, in view of NPL: L. Yinghui, "Thermal Conductivity Measurement about Fluid and Solid," 2013 Third International Conference on Intelligent System Design and Engineering Applications, Hong Kong, China, 2013, pp. 1570-1573, hereafter L.
Regarding Claim 3: D discloses a system according to Claim 2.
D does not disclose wherein the thermal conductivity is obtained using an empirical test conducted using the heating source.
However, L discloses wherein the thermal conductivity is obtained using an empirical test conducted using the heating source.
L [Page 1573: Section H] “A final transient technique is that which has become known as the laser-flash technique developed originally for measurements in solids but occasionally used on liquids, particularly at high temperatures. Figure 6 contains a schematic diagram of the instrument as it is available today in a commercial form. The sample is illuminated on one face with a laser pulse of very short duration and high intensity. The absorption of the laser energy on the front face of the sample causes the generation of heat at that front surface, which is subsequently transmitted throughout the sample to the back face of the sample where the temperature rise is detected with an infrared remote sensor. The interpretation of measurements is based on a one-dimensional solution of Equation 14.4 subject to an initial condition of an instantaneous heat pulse at one location. The temperature rise at the back face of a sample of thickness l and radius r, is therefore given by:
PNG
media_image2.png
38
258
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Greyscale
Where Q is the energy absorbed at the front surface at time zero. The thermal diffusivity of the sample, a, is then often deduced from the measurement of the time taken for the back face of the sample to reach one half of its maximum value. The technique has the very distinct advantage that it does not require physical contact between the test sample and the heater or detector. For this reason, it is a particularly appropriate technique for use at high temperatures or in aggressive environments.” Examiner notes that empirical testing is a method of testing hypotheses based on observations, experience and measurable data.
D and L are analogous to the claimed invention because they both pertain to the analysis of thermal energy usage of a building.
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the teachings of L with the building data of D because the measurement instruments and techniques of L are more accurate and less prone to error compared to older techniques to measure thermal conductivity. (See L [Page 1570: Section: Introduction])”
Regarding Claim 13: D discloses a method according to Claim 12.
D does not disclose wherein the thermal conductivity is obtained using an empirical test conducted using the heating source.
However, L discloses wherein the thermal conductivity is obtained using an empirical test conducted using the heating source.
L [Page 1573: Section H] “A final transient technique is that which has become known as the laser-flash technique developed originally for measurements in solids but occasionally used on liquids, particularly at high temperatures. Figure 6 contains a schematic diagram of the instrument as it is available today in a commercial form. The sample is illuminated on one face with a laser pulse of very short duration and high intensity. The absorption of the laser energy on the front face of the sample causes the generation of heat at that front surface, which is subsequently transmitted throughout the sample to the back face of the sample where the temperature rise is detected with an infrared remote sensor. The interpretation of measurements is based on a one-dimensional solution of Equation 14.4 subject to an initial condition of an instantaneous heat pulse at one location. The temperature rise at the back face of a sample of thickness l and radius r, is therefore given by:
PNG
media_image2.png
38
258
media_image2.png
Greyscale
Where Q is the energy absorbed at the front surface at time zero. The thermal diffusivity of the sample, a, is then often deduced from the measurement of the time taken for the back face of the sample to reach one half of its maximum value. The technique has the very distinct advantage that it does not require physical contact between the test sample and the heater or detector. For this reason, it is a particularly appropriate technique for use at high temperatures or in aggressive environments.”
D and L are analogous to the claimed invention because they both pertain to the analysis of thermal energy usage of a building.
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the teachings of L with the building data of D because the measurement instruments and techniques of L are more accurate and less prone to error compared to older techniques to measure thermal conductivity. (See L [Page 1570: Section: Introduction])”
Claims 4, 9-10, 14, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application 2014/0142905 A1, hereafter D, in view of U.S. Patent Application 2012/0330626 A1, hereafter A.
Regarding Claim 4: D discloses a system according to claim 1.
D does not disclose the computer further configured to model at least one change to the building, wherein the change is performed based on the comparison.
However, A discloses the computer further configured to model at least one change to the building, wherein the change is performed based on the comparison.
A [0036] “Using the static heat transfer model (Equation (10)), five parameters, λenv, λinf, λbase, λload, λsol are to be determined in one embodiment of the present disclosure. The parameter λenv is for the overall heat transfer; the λinf corresponds to infiltration m.sub.inf through the openings of the building envelop; the parameter λbase is for the non-thermal energy consumption; the λload is for sensible loads that have heating contribution; the λsol is for the overall solar contribution coefficient. In one embodiment of the present disclosure, in order to make them comparable across different buildings, the overall heat transfer and solar contribution are normalized by the area of the building envelope; the non-thermal base load and the sensible load are normalized by the geometrical average of GSF and NOP. Note that a fundamental difference between overall heat transfer and infiltration is from inclusion of DMH and HMH in the infiltration, since moisture could not get into the building via heat transfer (e.g., heat conduction and convection). Also, it should be noted that sensible load and solar contribution are different for cooling and heating energy usage. During cooling season, additional energy is needed to offset the heat from sensible load and solar radiation, while during heating season, less energy is used due to contribution from sensible load and solar radiation.
A [0020] “The analytic procedure 104 in one embodiment of the present disclosure results in thermal parameter estimation 134, which may be utilized or generated as one or more outputs 106. For instance, the derived physical model 130 may be used to generate usage distribution between heating and cooling 120. The derived physical model 130 may be also used to generate heat loss or gains through different parts of the building envelope 122. The derived physical model 130 may be further used in performing sensitivity analysis that determines how energy consumption changes as insulation condition changes 124, e.g., insulation condition of a wall, roof or window.
D and A are analogous to the claimed invention because they both pertain to the analysis of thermal energy usage of a building.
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the teachings of A with D because the estimation of heat transfer parameters helps to save energy and improve energy efficiency through understanding how heat transfers throughout buildings, which the estimation helps to overcome gaps in limited observable data and insufficient building information (See A [0002]).
Regarding Claim 9: D discloses a system according to Claim 1, wherein the internal heating gains are identified using occupant heating gains for the building.
D [0172] “Any of building statistics 212 relating to occupancy estimate 1216 may be used by statistics analyzer 214, outlier detector 218, or report generator 216. For example, the value may be compared by statistics analyzer 214 to that of other buildings in the same class. Similarly, the value may be used by outlier detector 218 to determine whether the building is an outlier among its class. In various embodiments, the occupancy-related value in building statistics 212 may be used by statistics analyzer 214 and outlier detector 218 as part of a univariate or multivariate analysis. For example, statistics analyzer 214 may compare only the occupancy-related intensity values (I--βw) among the buildings in a class of buildings identified by building classifier 208. In another example, outlier detector 218 may use the occupancy-related values with other building statistics 212 (e.g., cooling slope values, cooling balance point values, etc.). Report generator 216 may also report on the occupancy-related value in a similar manner to any of the other values in building statistics 212. For example, report generator 216 may generate a bivariate scatter plot using a building's occupancy-related values.”
D [0119] “A heating slope (SH) 512 may correspond to the change in energy use or energy costs that result when the outdoor air temperature drops below a heating balance point 508 (e.g., a breakeven temperature). For example, assume that heating balance point 508 for a building is 55oF. When the outdoor air temperature is at or above 55oF, only an energy expenditure equal to base load 506 may be needed to maintain the internal temperature of the building. However, additional energy may be needed, if the outdoor air temperature drops below 55oF. (e.g., to provide mechanical heating to the interior of the building). As the outdoor air temperature decreases, the amount of energy needed to heat the building likewise increases at a rate corresponding to heating slope 512.
D does not disclose heating gains produced by operation of electric devices in the building and solar heating gains for the building.
However, A discloses heating gains produced by operation of electric devices in the building solar heating gains for the building.
A [0036] “Using the static heat transfer model (Equation (10)), five parameters, λenv, λinf, λbase, λload, λsol are to be determined in one embodiment of the present disclosure. The parameter λenv is for the overall heat transfer; the λinf corresponds to infiltration m.sub.inf through the openings of the building envelop; the parameter λbase is for the non-thermal energy consumption; the λload is for sensible loads that have heating contribution; the λsol is for the overall solar contribution coefficient. In one embodiment of the present disclosure, in order to make them comparable across different buildings, the overall heat transfer and solar contribution are normalized by the area of the building envelope; … During cooling season, additional energy is needed to offset the heat from sensible load and solar radiation, while during heating season, less energy is used due to contribution from sensible load and solar radiation. Examiner notes that λload pertains to sensible loads, which is heat that comes from both electronic devices and occupants (See A [Equation 1]).
D and A are analogous to the claimed invention because they both pertain to the analysis of thermal energy usage of a building.
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the teachings of A with D because the estimation of heat transfer parameters helps to save energy and improve energy efficiency through understanding how heat transfers throughout buildings, which the estimation helps to overcome gaps in limited observable data and insufficient building information (See A [0002]).
Regarding Claim 10: D discloses a system according to Claim 1.
D does not disclose wherein the total thermal conductivity comprises the balance point thermal conductivity and thermal conductivity for auxiliary heating.
However, A discloses wherein the total thermal conductivity comprises the balance point thermal conductivity and thermal conductivity for auxiliary heating.
A [0021] “A heat transfer model may be derived for the total energy usage in a period (e.g., monthly), associated to heating gain or loss via conduction, convection and radiation through the building envelop.
A [0036] “Using the static heat transfer model (Equation (10)), five parameters, λenv, λinf, λbase, λload, λsol are to be determined in one embodiment of the present disclosure. The parameter λenv is for the overall heat transfer; the λinf corresponds to infiltration m.sub.inf through the openings of the building envelop; the parameter λbase is for the non-thermal energy consumption; the λload is for sensible loads that have heating contribution; the λsol is for the overall solar contribution coefficient. In one embodiment of the present disclosure, in order to make them comparable across different buildings, the overall heat transfer and solar contribution are normalized by the area of the building envelope; the non-thermal base load and the sensible load are normalized by the geometrical average of GSF and NOP. Note that a fundamental difference between overall heat transfer and infiltration is from inclusion of DMH and HMH in the infiltration, since moisture could not get into the building via heat transfer (e.g., heat conduction and convection). Also, it should be noted that sensible load and solar contribution are different for cooling and heating energy usage. During cooling season, additional energy is needed to offset the heat from sensible load and solar radiation, while during heating season, less energy is used due to contribution from sensible load and solar radiation. Examiner notes that λload pertains to sensible loads, which is heat that comes from both electronic devices and occupants (See A [Equation 1]), which in this case would cover the thermal conductivity of auxiliary heating. The thermal conductivity of auxiliary heating in this case contributes to the λenv, the overall heat transfer.
D and A are analogous to the claimed invention because they both pertain to the analysis of thermal energy usage of a building.
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the teachings of A with D because the estimation of heat transfer parameters helps to save energy and improve energy efficiency through understanding how heat transfers throughout buildings, which the estimation helps to overcome gaps in limited observable data and insufficient building information (See A [0002]).
Regarding Claim 14: D discloses a method according to claim 11.
D does not disclose the computer further configured to model at least one change to the building, wherein the change is performed based on the comparison.
However, A discloses the computer further configured to model at least one change to the building, wherein the change is performed based on the comparison.
A [0036] “Using the static heat transfer model (Equation (10)), five parameters, λenv, λinf, λbase, λload, λsol are to be determined in one embodiment of the present disclosure. The parameter λenv is for the overall heat transfer; the λinf corresponds to infiltration m.sub.inf through the openings of the building envelop; the parameter λbase is for the non-thermal energy consumption; the λload is for sensible loads that have heating contribution; the λsol is for the overall solar contribution coefficient. In one embodiment of the present disclosure, in order to make them comparable across different buildings, the overall heat transfer and solar contribution are normalized by the area of the building envelope; the non-thermal base load and the sensible load are normalized by the geometrical average of GSF and NOP. Note that a fundamental difference between overall heat transfer and infiltration is from inclusion of DMH and HMH in the infiltration, since moisture could not get into the building via heat transfer (e.g., heat conduction and convection). Also, it should be noted that sensible load and solar contribution are different for cooling and heating energy usage. During cooling season, additional energy is needed to offset the heat from sensible load and solar radiation, while during heating season, less energy is used due to contribution from sensible load and solar radiation.
A [0020] “The analytic procedure 104 in one embodiment of the present disclosure results in thermal parameter estimation 134, which may be utilized or generated as one or more outputs 106. For instance, the derived physical model 130 may be used to generate usage distribution between heating and cooling 120. The derived physical model 130 may be also used to generate heat loss or gains through different parts of the building envelope 122. The derived physical model 130 may be further used in performing sensitivity analysis that determines how energy consumption changes as insulation condition changes 124, e.g., insulation condition of a wall, roof or window.
D and A are analogous to the claimed invention because they both pertain to the analysis of thermal energy usage of a building.
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the teachings of A with D because the estimation of heat transfer parameters helps to save energy and improve energy efficiency through understanding how heat transfers throughout buildings, which the estimation helps to overcome gaps in limited observable data and insufficient building information (See A [0002]).
Regarding Claim 19: D discloses a method according to Claim 11, wherein the internal heating gains are identified using occupant heating gains for the building and heating gains produced by operation of electric devices in the building.
D [0172] “Any of building statistics 212 relating to occupancy estimate 1216 may be used by statistics analyzer 214, outlier detector 218, or report generator 216. For example, the value may be compared by statistics analyzer 214 to that of other buildings in the same class. Similarly, the value may be used by outlier detector 218 to determine whether the building is an outlier among its class. In various embodiments, the occupancy-related value in building statistics 212 may be used by statistics analyzer 214 and outlier detector 218 as part of a univariate or multivariate analysis. For example, statistics analyzer 214 may compare only the occupancy-related intensity values (I--βw) among the buildings in a class of buildings identified by building classifier 208. In another example, outlier detector 218 may use the occupancy-related values with other building statistics 212 (e.g., cooling slope values, cooling balance point values, etc.). Report generator 216 may also report on the occupancy-related value in a similar manner to any of the other values in building statistics 212. For example, report generator 216 may generate a bivariate scatter plot using a building's occupancy-related values.”
D [0119] “A heating slope (SH) 512 may correspond to the change in energy use or energy costs that result when the outdoor air temperature drops below a heating balance point 508 (e.g., a breakeven temperature). For example, assume that heating balance point 508 for a building is 55oF. When the outdoor air temperature is at or above 55oF, only an energy expenditure equal to base load 506 may be needed to maintain the internal temperature of the building. However, additional energy may be needed, if the outdoor air temperature drops below 55oF. (e.g., to provide mechanical heating to the interior of the building). As the outdoor air temperature decreases, the amount of energy needed to heat the building likewise increases at a rate corresponding to heating slope 512.
D does not disclose solar heating gains for the building.
However, A discloses solar heating gains for the building.
A [0036] “Using the static heat transfer model (Equation (10)), five parameters, λenv, λinf, λbase, λload, λsol are to be determined in one embodiment of the present disclosure. The parameter λenv is for the overall heat transfer; the λinf corresponds to infiltration m.sub.inf through the openings of the building envelop; the parameter λbase is for the non-thermal energy consumption; the λload is for sensible loads that have heating contribution; the λsol is for the overall solar contribution coefficient. In one embodiment of the present disclosure, in order to make them comparable across different buildings, the overall heat transfer and solar contribution are normalized by the area of the building envelope; the non-thermal base load and the sensible load are normalized by the geometrical average of GSF and NOP. Note that a fundamental difference between overall heat transfer and infiltration is from inclusion of DMH and HMH in the infiltration, since moisture could not get into the building via heat transfer (e.g., heat conduction and convection). Also, it should be noted that sensible load and solar contribution are different for cooling and heating energy usage. During cooling season, additional energy is needed to offset the heat from sensible load and solar radiation, while during heating season, less energy is used due to contribution from sensible load and solar radiation.
D and A are analogous to the claimed invention because they both pertain to the analysis of thermal energy usage of a building.
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the teachings of A with D because the estimation of heat transfer parameters helps to save energy and improve energy efficiency through understanding how heat transfers throughout buildings, which the estimation helps to overcome gaps in limited observable data and insufficient building information (See A [0002]).
Regarding Claim 20: D discloses a method according to Claim 11.
D does not disclose wherein the total thermal conductivity comprises the balance point thermal conductivity and thermal conductivity for auxiliary heating.
However, A discloses wherein the total thermal conductivity comprises the balance point thermal conductivity and thermal conductivity for auxiliary heating.
A [0021] “A heat transfer model may be derived for the total energy usage in a period (e.g., monthly), associated to heating gain or loss via conduction, convection and radiation through the building envelop.
A [0036] “Using the static heat transfer model (Equation (10)), five parameters, λenv, λinf, λbase, λload, λsol are to be determined in one embodiment of the present disclosure. The parameter λenv is for the overall heat transfer; the λinf corresponds to infiltration m.sub.inf through the openings of the building envelop; the parameter λbase is for the non-thermal energy consumption; the λload is for sensible loads that have heating contribution; the λsol is for the overall solar contribution coefficient. In one embodiment of the present disclosure, in order to make them comparable across different buildings, the overall heat transfer and solar contribution are normalized by the area of the building envelope; the non-thermal base load and the sensible load are normalized by the geometrical average of GSF and NOP. Note that a fundamental difference between overall heat transfer and infiltration is from inclusion of DMH and HMH in the infiltration, since moisture could not get into the building via heat transfer (e.g., heat conduction and convection). Also, it should be noted that sensible load and solar contribution are different for cooling and heating energy usage. During cooling season, additional energy is needed to offset the heat from sensible load and solar radiation, while during heating season, less energy is used due to contribution from sensible load and solar radiation.
D and A are analogous to the claimed invention because they both pertain to the analysis of thermal energy usage of a building.
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the teachings of A with D because the estimation of heat transfer parameters helps to save energy and improve energy efficiency through understanding how heat transfers throughout buildings, which the estimation helps to overcome gaps in limited observable data and insufficient building information (See A [0002]).
Conclusion
All Claims are rejected.
The prior art made record of and not relied upon is considered pertinent to the applicant’s disclosure.
WO 2012/049417 A2
This reference discloses a device used for identifying the thermal conductivity and/or heat capacity of a planar wall.
Hafemeister, D. (2003). Energy Scaling Law for Buildings. Hyperfine Interactions, 151(1), 191-193
This reference models energy use in buildings in order to save energy with well-designed buildings.
Kissock, J. K., & Mulqueen, S. (2008). Targeting energy efficiency in commercial buildings using advanced billing analysis. In Proceedings of the 2008 ACEEE Summer Study on Energy Efficiency in Buildings (pp. 2-205). American Council for an Energy-Efficient Economy.
This reference discloses a four step method to analyze the utility bills and weather data from multiple buildings to target the measure and effectiveness of energy efficiency opportunities.
Huang, H., Ooka, R., & Kato, S. (2005). Urban thermal environment measurements and numerical simulation for an actual complex urban area covering a large district heating and cooling system in summer. Atmospheric Environment, 39(34), 6362–6375.
This reference discloses a method to analyze an urban thermal environment through analysis from field measurements and a numerical simulation program to predict the urban thermal environment.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Scott T. Tran whose telephone number is (571) 272-8533. The examiner can normally be reached on M-F, 8:00-4:00.
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://uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Renee Chavez, can be reached at (571) 270-1104. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Informal or draft communication, please label PROPOSED or DRAFT, can be additionally sent to the Examiner’s fax phone number (571) 272-8533.
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STT
/SCOTT THANH BINH TRAN/Examiner, Art Unit 2186
/RENEE D CHAVEZ/Supervisory Patent Examiner, Art Unit 2186
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