DETAILED ACTION
In response to the Appeal Brief filed on June 9, 2026, prosecution is hereby reopened. This instant Office action is non-final. A new examiner has taken over prosecution of the instant application. The after-final amendment filed on March 13, 2026 has been entered. Claims 1-22 are presented for examination. New rejections are applied below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claimed invention is directed to “increasing sustainability of buildings” and “obtaining building operational data from a plurality of building management systems and providing scores for a plurality of potential sustainable infrastructure projects by scoring the potential sustainable infrastructure projects based on at least one of utility information, climate data, building characteristics, or the building operational data” (Spec: ¶ 3) without significantly more.
Step
Analysis
1: Statutory Category?
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Process (claims 1-11, 18-22), Article of Manufacture (claims 12-17)
Independent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claim 1] A method for increasing sustainability of buildings, comprising:
obtaining building operational data from a plurality of building management sources;
providing scores for a plurality of potential sustainable infrastructure projects by scoring the potential sustainable infrastructure projects based on at least one of utility information, climate data, building characteristics, or the building operational data, wherein the scores are generated at least in part on the building operational data;
providing a ranking of the potential sustainable infrastructure projects based on the scores.
[Claim 12] obtaining building operational data from a plurality of building management sources;
providing a ranking of potential sustainable infrastructure projects based on scores for a plurality of potential sustainable infrastructure projects calculated based on at least one of utility information, climate data, building characteristics, or the building operational data, wherein the scores are generated at least in part on the building operational data.
[Claim 18] A method of executing sustainable infrastructure projects, comprising:
ranking the sustainable infrastructure projects by scoring the sustainable infrastructure projects based on utility information, climate data, building characteristics, and building operational data associated with the sustainable infrastructure projects; and
installing building equipment or modifying at least one building to execute the sustainable infrastructure projects in an order indicated by the ranking.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can obtain data, provide scores, and rank projects based on scores. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to “increasing sustainability of buildings” and “obtaining building operational data from a plurality of building management systems and providing scores for a plurality of potential sustainable infrastructure projects by scoring the potential sustainable infrastructure projects based on at least one of utility information, climate data, building characteristics, or the building operational data” (Spec: ¶ 3), which (under its broadest reasonable interpretation) is an example of business relations (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity. Additionally, regarding the step of installing in claim 18, a human user could be instructed to install building equipment or modify at least one building.
Ranking projects based on scores implies mathematical comparison, which is an example of a mathematical concept.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Claim 1 recites obtaining building operational data from a plurality of building management systems and providing, via a graphical user interface, a ranking of the potential sustainable infrastructure projects based on the scores.
Claim 12 recites one or more non-transitory computer-readable media storing program instructions that, when executed by one or more processors, perform the recited operations. Claim 12 also recites obtaining building operational data from a plurality of building management systems and providing, via a graphical user interface, a ranking of potential sustainable infrastructure projects based on scores for a plurality of potential sustainable infrastructure projects.
Regarding claim 18, even if the step of “installing building equipment or modifying at least one building to execute the sustainable infrastructure projects in an order indicated by the ranking” is seen as requiring more than instructing a human to perform the installing or modifying, any implied additional elements would (at best) be a mere tool to apply the exception. This is akin to “a method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016) (non-precedential).” (See MPEP § 2106.05(f)(2).)
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 30-34).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
The claims also generally receive, transmit, store, and/or output (e.g., display) data, which are examples of insignificant extra-solution activity.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
As explained above, there is nothing in the claims as a whole that adds significantly more to the abstract idea(s). Evidence regarding operations of the additional elements that are well-understood, routine, and conventional is provided below.
MPEP § 2106.05(d)(II) sets forth the following:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
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Dependent claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claim 2] executing a subset of the potential sustainable infrastructure projects in an order based on the ranking.
[Claim 3] wherein:
the plurality of potential sustainable infrastructure projects comprise a first project for a first building and a second project for a second building;
the utility information comprises a first utility rate for the first building and a second utility rate for the second building; and
scoring the potential sustainable infrastructure projects comprises improving a first score for the first project relative to a second score for the second project if the first utility rate is higher than the second utility rate.
[Claim 4] wherein:
the plurality of potential sustainable infrastructure projects comprise a first project for a first building and a second project for a second building;
the utility information comprises a first grid emissions rate associated with energy generated for provision to the first building and a second grid emissions rate associated with energy generated for provision to the second building; and
scoring the potential sustainable infrastructure projects comprises improving a first score for the first project relative to a second score for the second project if the first grid emissions rate is higher than the second grid emissions rate.
[Claim 5] wherein the utility information indicates one or more of incentive program information, demand response program information, frequency regulation program information, demand charge information, and energy buy-back rates.
[Claim 6] wherein providing the scores is further based on historical pitch success rates for infrastructure projects.
[Claim 7] wherein providing the scores further comprises accounting for retrofit constraints indicated by the building characteristics.
[Claim 10] predicting savings expected to result from the plurality of potential sustainable infrastructure projects.
[Claim 11] wherein providing the scores further comprises determining prices for the plurality of potential sustainable infrastructure projects.
[Claim 22] installing building equipment or modifying at least one of the buildings to execute the potential sustainable infrastructure projects in an order indicated by the ranking.
[Claim 13] wherein the operations further comprise initiating at least some of the potential sustainable infrastructure projects in an order based on the ranking.
[Claim 14] wherein:
the plurality of potential sustainable infrastructure projects comprise a first project for a first building and a second project for a second building;
the utility information comprises a first utility rate for the first building and a second utility rate for the second building; and
the operations further comprise improving a first score for the first project relative to a second score for the second project if the first utility rate is higher than the second utility rate.
[Claim 15] wherein:
the plurality of potential sustainable infrastructure projects comprise a first project for a first building and a second project for a second building;
the utility information comprises a first grid emissions rate associated with energy generated for provision to the first building and a second grid emissions rate associated with energy generated for provision to the second building; and
the operations further comprise improving a first score for the first project relative to a second score for the second project if the first grid emissions rate is higher than the second grid emissions rate.
[Claim 16] wherein providing the scores further comprises accounting for retrofit constraints indicated by the building characteristics and comparing the utility information, the climate data, and the building characteristics.
[Claim 17] the operations further comprise predicting savings expected to result from the plurality of potential sustainable infrastructure projects, determining prices for the plurality of potential sustainable infrastructure projects, and comparing the savings with the prices.
[Claim 19] wherein:
the sustainable infrastructure projects comprise a first project for a first building and a second project for a second building;
the utility information comprises a first grid emissions rate associated with energy generated for provision to the first building and a second grid emissions rate associated with energy generated for provision to the second building; and
scoring the sustainable infrastructure projects comprises improving a first score for the first project relative to a second score for the second project if the first grid emissions rate is higher than the second grid emissions rate.
[Claim 20] wherein scoring the sustainable infrastructure projects is based on retrofit constraints indicated by the building characteristics or the building operational data.
[Claim 21] providing the ranking of the sustainable infrastructure projects based on the scoring.
The dependent claims further present details of the abstract ideas identified in regard to the independent claims.
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can obtain data, provide scores, and rank projects based on scores. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to “increasing sustainability of buildings” and “obtaining building operational data from a plurality of building management systems and providing scores for a plurality of potential sustainable infrastructure projects by scoring the potential sustainable infrastructure projects based on at least one of utility information, climate data, building characteristics, or the building operational data” (Spec: ¶ 3), which (under its broadest reasonable interpretation) is an example of business relations (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity. Additionally, regarding the step of executing a subset of the potential sustainable infrastructure projects in an order based on the ranking in claim 2, a human user could be instructed to execute a subset of the potential sustainable infrastructure projects in an order based on the ranking. Claim 22 recites “installing building equipment or modifying at least one of the buildings to execute the potential sustainable infrastructure projects in an order indicated by the ranking” and a human user could be instructed to install building equipment or modify at least one of the buildings to execute the potential sustainable infrastructure projects in an order indicated by the ranking.
Ranking projects based on scores implies mathematical comparison, which is an example of a mathematical concept.
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
The dependent claims include the additional elements of their independent claims.
Claim 1 recites obtaining building operational data from a plurality of building management systems and providing, via a graphical user interface, a ranking of the potential sustainable infrastructure projects based on the scores.
Regarding claim 2, even if the step of “executing a subset of the potential sustainable infrastructure projects in an order based on the ranking” is seen as requiring more than instructing a human to perform the execution of projects, any implied additional elements would (at best) be a mere tool to apply the exception. This is akin to “a method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016) (non-precedential).” (See MPEP § 2106.05(f)(2).)
Claim 8 recites wherein providing the scores further comprises grouping, by a neural network using the building characteristics, the potential sustainable infrastructure projects with historical project data associated with other buildings.
Claim 9 recites training a neural network to classify sets of the climate data and the building characteristics between being feasible and unfeasible for different types of the potential sustainable infrastructure projects, wherein the scoring is performed using the neural network.
Regarding claim 22, even if the step of “installing building equipment or modifying at least one of the buildings to execute the potential sustainable infrastructure projects in an order indicated by the ranking” is seen as requiring more than instructing a human to perform the installing or modifying, any implied additional elements would (at best) be a mere tool to apply the exception. This is akin to “a method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016) (non-precedential).” (See MPEP § 2106.05(f)(2).)
Claim 12 recites one or more non-transitory computer-readable media storing program instructions that, when executed by one or more processors, perform the recited operations. Claim 12 also recites obtaining building operational data from a plurality of building management systems and providing, via a graphical user interface, a ranking of potential sustainable infrastructure projects based on scores for a plurality of potential sustainable infrastructure projects.
Regarding claim 13, even if the step of “initiating at least some of the potential sustainable infrastructure projects in an order based on the ranking” is seen as requiring more than instructing a human to perform the initiating, any implied additional elements would (at best) be a mere tool to apply the exception. This is akin to “a method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016) (non-precedential).” (See MPEP § 2106.05(f)(2).)
Regarding claim 18, even if the step of “installing building equipment or modifying at least one building to execute the sustainable infrastructure projects in an order indicated by the ranking” is seen as requiring more than instructing a human to perform the installing or modifying, any implied additional elements would (at best) be a mere tool to apply the exception. This is akin to “a method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016) (non-precedential).” (See MPEP § 2106.05(f)(2).)
Claim 21 recites providing, via a graphical user interface, the ranking of the sustainable infrastructure projects based on the scoring.
Regarding claim 22, even if the step of “installing building equipment or modifying at least one of the buildings to execute the sustainable infrastructure projects in an order indicated by the ranking” is seen as requiring more than instructing a human to perform the installing or modifying, any implied additional elements would (at best) be a mere tool to apply the exception. This is akin to “a method of assigning hair designs to balance head shape with a final step of using a tool (scissors) to cut the hair, In re Brown, 645 Fed. App'x 1014, 1017 (Fed. Cir. 2016) (non-precedential).” (See MPEP § 2106.05(f)(2).)
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 30-34).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Claims 8 and 9 use a neural network at a high level of generality. Considering that the implementation of the machine learning model and/or the training of the model is performed using generic processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: ¶¶ 123, 125). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
The claims also generally receive, transmit, store, and/or output (e.g., display) data, which are examples of insignificant extra-solution activity.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
As explained above, there is nothing in the claims as a whole that adds significantly more to the abstract idea(s). Evidence regarding operations of the additional elements that are well-understood, routine, and conventional is provided below.
MPEP § 2106.05(d)(II) sets forth the following:
The courts have recognized the following computer functions as well‐understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity.
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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, 7-10, 12, and 16 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Abramson et al. (US 2019/0347670).
[Claim 1] Abramson discloses a method for increasing sustainability of buildings (abstract – “The energy conservation prognostics may include one or more energy conservation measure recommendations and corresponding predicted cost/energy savings.”), comprising:
obtaining building operational data from a plurality of building management systems (figs. 3, 4 – Various types of data are retrieved from various sources.; ¶ 27 – “In an example embodiment, a method for providing virtual energy audits may include steps for retrieving, by a processor of an analytics server, weather data corresponding to at least one target building from a weather server coupled to the analytics server, receiving, by the processor, utility energy usage data corresponding to the at least one target building, obtaining a set of building-specific characteristic data corresponding to the at least one target building, combining the weather data, utility energy usage data, and building-specific characteristic data into an energy audit dataset, determining a plurality of building markers for the at least one target building based on the energy audit dataset, generating prognostics data based on the energy audit dataset and building markers, the prognostics data including energy conservation recommendations and estimated impacts of implementing the energy conservation recommendations, generating building efficiency diagnostics based on the plurality of building markers, and sending the building efficiency diagnostics and the energy conservation recommendations to be displayed on a user interface of a client device coupled to the analytics server. The building efficiency diagnostics may include estimated heating/cooling system characteristics of the at least one target building.”; ¶ 30 – “In some embodiments, the at least one target building may include a plurality of target buildings. The building efficiency diagnostics may include a plurality of subsets of building efficiency diagnostics. The energy conservation recommendations may include a plurality of subsets of energy conservation recommendations. Each of the plurality of target buildings may correspond to a respectively different subset of building efficiency diagnostics of the plurality of subsets of building efficiency diagnostics. Each of the plurality of target buildings may correspond to a respectively different subset of energy conservation recommendations of the plurality of subsets of energy conservation recommendations.”);
providing scores for a plurality of potential sustainable infrastructure projects by scoring the potential sustainable infrastructure projects based on at least one of utility information, climate data, building characteristics, or the building operational data, wherein the scores are generated at least in part on the building operational data (fig. 3 – Information is displayed on the client device.; ¶ 135 – “The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”; ¶ 27 – “In an example embodiment, a method for providing virtual energy audits may include steps for retrieving, by a processor of an analytics server, weather data corresponding to at least one target building from a weather server coupled to the analytics server, receiving, by the processor, utility energy usage data corresponding to the at least one target building, obtaining a set of building-specific characteristic data corresponding to the at least one target building, combining the weather data, utility energy usage data, and building-specific characteristic data into an energy audit dataset, determining a plurality of building markers for the at least one target building based on the energy audit dataset, generating prognostics data based on the energy audit dataset and building markers, the prognostics data including energy conservation recommendations and estimated impacts of implementing the energy conservation recommendations, generating building efficiency diagnostics based on the plurality of building markers, and sending the building efficiency diagnostics and the energy conservation recommendations to be displayed on a user interface of a client device coupled to the analytics server.”; ¶ 30 – “In some embodiments, the at least one target building may include a plurality of target buildings. The building efficiency diagnostics may include a plurality of subsets of building efficiency diagnostics. The energy conservation recommendations may include a plurality of subsets of energy conservation recommendations. Each of the plurality of target buildings may correspond to a respectively different subset of building efficiency diagnostics of the plurality of subsets of building efficiency diagnostics. Each of the plurality of target buildings may correspond to a respectively different subset of energy conservation recommendations of the plurality of subsets of energy conservation recommendations. The method may further include steps for quantifying a respective savings opportunity value for each of the plurality of target buildings to produce a plurality of savings opportunity values, the energy conservation recommendations including the plurality of savings opportunity values, generating an ordered list of the plurality of target buildings that is ordered based on the plurality of savings opportunity values, identifying a subset of target buildings of the plurality of target buildings associated with the highest savings opportunity values of the plurality of savings opportunity values, and sending the ordered list and the identified subset of target buildings to be displayed via the user interface of the client device.”; The quantified savings and energy amounts are examples of scores.);
providing, via a graphical user interface, a ranking of the potential sustainable infrastructure projects based on the scores (fig. 3 – Information is displayed on the client device.; ¶ 135 – “The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”; ¶ 27 – “In an example embodiment, a method for providing virtual energy audits may include steps for retrieving, by a processor of an analytics server, weather data corresponding to at least one target building from a weather server coupled to the analytics server, receiving, by the processor, utility energy usage data corresponding to the at least one target building, obtaining a set of building-specific characteristic data corresponding to the at least one target building, combining the weather data, utility energy usage data, and building-specific characteristic data into an energy audit dataset, determining a plurality of building markers for the at least one target building based on the energy audit dataset, generating prognostics data based on the energy audit dataset and building markers, the prognostics data including energy conservation recommendations and estimated impacts of implementing the energy conservation recommendations, generating building efficiency diagnostics based on the plurality of building markers, and sending the building efficiency diagnostics and the energy conservation recommendations to be displayed on a user interface of a client device coupled to the analytics server.”; ¶ 30 – “In some embodiments, the at least one target building may include a plurality of target buildings. The building efficiency diagnostics may include a plurality of subsets of building efficiency diagnostics. The energy conservation recommendations may include a plurality of subsets of energy conservation recommendations. Each of the plurality of target buildings may correspond to a respectively different subset of building efficiency diagnostics of the plurality of subsets of building efficiency diagnostics. Each of the plurality of target buildings may correspond to a respectively different subset of energy conservation recommendations of the plurality of subsets of energy conservation recommendations. The method may further include steps for quantifying a respective savings opportunity value for each of the plurality of target buildings to produce a plurality of savings opportunity values, the energy conservation recommendations including the plurality of savings opportunity values, generating an ordered list of the plurality of target buildings that is ordered based on the plurality of savings opportunity values, identifying a subset of target buildings of the plurality of target buildings associated with the highest savings opportunity values of the plurality of savings opportunity values, and sending the ordered list and the identified subset of target buildings to be displayed via the user interface of the client device.”; The quantified savings and energy amounts are examples of scores.).
[Claim 2] Abramson discloses executing a subset of the potential sustainable infrastructure projects in an order based on the ranking (¶ 135 – “The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”; ¶ 30 – “In some embodiments, the at least one target building may include a plurality of target buildings. The building efficiency diagnostics may include a plurality of subsets of building efficiency diagnostics. The energy conservation recommendations may include a plurality of subsets of energy conservation recommendations. Each of the plurality of target buildings may correspond to a respectively different subset of building efficiency diagnostics of the plurality of subsets of building efficiency diagnostics. Each of the plurality of target buildings may correspond to a respectively different subset of energy conservation recommendations of the plurality of subsets of energy conservation recommendations. The method may further include steps for quantifying a respective savings opportunity value for each of the plurality of target buildings to produce a plurality of savings opportunity values, the energy conservation recommendations including the plurality of savings opportunity values, generating an ordered list of the plurality of target buildings that is ordered based on the plurality of savings opportunity values, identifying a subset of target buildings of the plurality of target buildings associated with the highest savings opportunity values of the plurality of savings opportunity values, and sending the ordered list and the identified subset of target buildings to be displayed via the user interface of the client device.”; The quantified savings and energy amounts are examples of scores.).
[Claim 7] Abramson discloses wherein providing the scores further comprises accounting for retrofit constraints indicated by the building characteristics (¶ 134 – “With additional metadata for, or an estimate of, window-to-wall ratio in the building, a parallel thermal resistance network analysis can be employed to determine the corresponding effective R-value of the windows in the building. Consequently, this analysis ultimately reveals the potential value of, and opportunity for, window replacement which is a common energy conservation measure (ECM) recommendation. For example, this analysis was applied to one real world buildings, and the resulting findings were then validated against an energy audit conducted by an on-site energy audit firm, to reveal that as-calculated low R-value buildings correspond well to those buildings which the company determined are in need of retro-commissioning or deep retrofit to achieve energy efficiency goals set by the building owner. The firm's on-site energy audit required engineers to access the building in-person, over several days per building and at a cost of thousands of dollars per building. The systems and methods disclosed herein are capable of diagnosing similar problems with the building within minutes and at a much reduced cost.”; ¶ 135 – “While the above examples have been provided in the context of analyzing a single building, it should be understood that multiple buildings may be similarly analyzed, with energy conservation prognostics and building efficiency diagnostics and corresponding reports being generated, stored, and/or displayed for each. As an example, a system for providing virtual energy audits (e.g., the system 300 of FIG. 3) may receive a list of target buildings from a client device, along with predefined building characteristics for each of the target buildings. The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”).
[Claim 8] Abramson discloses wherein providing the scores further comprises grouping, by a neural network using the building characteristics, the potential sustainable infrastructure projects with historical project data associated with other buildings (¶ 96 – “For example, the analytics engine 320 may execute a Forecast-ml function to create a prediction/forecast of energy usage of the audited building for a period of time (e.g., one week) under a given set of conditions using a neural network approach. For example a multi-layer perceptron neural network (MLP-NN) may be implemented for the prediction of future energy usage of the audited building. For example, the MLP-NN may take as inputs feature vectors including variables derived from the energy usage data and the weather data. The MLP-NN may be trained specifically for the prediction of energy usage of a building based on historical energy usage for that building and historical weather data associated with that building. In alternate embodiments, a gradient-boosted regression trees (GBRT) algorithm may be applied instead of the MLP-NN to predict energy usage of the audited building given the defined circumstances. For example, the set of conditions may correspond to changes in the operation of or the retrofitting of one or more systems of the audited building. For example, the output of the Forecast-ml function may include predicted cost savings and/or predicted energy savings that may be achieved by following the recommendations associated with the set of conditions.”; ¶ 135 – “While the above examples have been provided in the context of analyzing a single building, it should be understood that multiple buildings may be similarly analyzed, with energy conservation prognostics and building efficiency diagnostics and corresponding reports being generated, stored, and/or displayed for each. As an example, a system for providing virtual energy audits (e.g., the system 300 of FIG. 3) may receive a list of target buildings from a client device, along with predefined building characteristics for each of the target buildings. The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building.“; The quantified savings and energy amounts are examples of scores.).
[Claim 9] Abramson discloses training a neural network to classify sets of the climate data and the building characteristics between being feasible and unfeasible for different types of the potential sustainable infrastructure projects, wherein the scoring is performed using the neural network (¶ 96 – “For example, the analytics engine 320 may execute a Forecast-ml function to create a prediction/forecast of energy usage of the audited building for a period of time (e.g., one week) under a given set of conditions using a neural network approach. For example a multi-layer perceptron neural network (MLP-NN) may be implemented for the prediction of future energy usage of the audited building. For example, the MLP-NN may take as inputs feature vectors including variables derived from the energy usage data and the weather data. The MLP-NN may be trained specifically for the prediction of energy usage of a building based on historical energy usage for that building and historical weather data associated with that building. In alternate embodiments, a gradient-boosted regression trees (GBRT) algorithm may be applied instead of the MLP-NN to predict energy usage of the audited building given the defined circumstances. For example, the set of conditions may correspond to changes in the operation of or the retrofitting of one or more systems of the audited building. For example, the output of the Forecast-ml function may include predicted cost savings and/or predicted energy savings that may be achieved by following the recommendations associated with the set of conditions.”; ¶ 135 – “While the above examples have been provided in the context of analyzing a single building, it should be understood that multiple buildings may be similarly analyzed, with energy conservation prognostics and building efficiency diagnostics and corresponding reports being generated, stored, and/or displayed for each. As an example, a system for providing virtual energy audits (e.g., the system 300 of FIG. 3) may receive a list of target buildings from a client device, along with predefined building characteristics for each of the target buildings. The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building.“; The quantified savings and energy amounts are examples of scores.; ¶ 134 – “With additional metadata for, or an estimate of, window-to-wall ratio in the building, a parallel thermal resistance network analysis can be employed to determine the corresponding effective R-value of the windows in the building. Consequently, this analysis ultimately reveals the potential value of, and opportunity for, window replacement which is a common energy conservation measure (ECM) recommendation. For example, this analysis was applied to one real world buildings, and the resulting findings were then validated against an energy audit conducted by an on-site energy audit firm, to reveal that as-calculated low R-value buildings correspond well to those buildings which the company determined are in need of retro-commissioning or deep retrofit to achieve energy efficiency goals set by the building owner. The firm's on-site energy audit required engineers to access the building in-person, over several days per building and at a cost of thousands of dollars per building. The systems and methods disclosed herein are capable of diagnosing similar problems with the building within minutes and at a much reduced cost.”; Recommended and validated solutions are examples of feasible projects (as opposed to solutions and projects that are not recommended and/or validated).).
[Claim 10] Abramson discloses predicting savings expected to result from the plurality of potential sustainable infrastructure projects (¶ 135 – “While the above examples have been provided in the context of analyzing a single building, it should be understood that multiple buildings may be similarly analyzed, with energy conservation prognostics and building efficiency diagnostics and corresponding reports being generated, stored, and/or displayed for each. As an example, a system for providing virtual energy audits (e.g., the system 300 of FIG. 3) may receive a list of target buildings from a client device, along with predefined building characteristics for each of the target buildings. The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”).
[Claim 12] Abramson discloses one or more non-transitory computer-readable media storing program instructions that, when executed by one or more processors (¶¶ 43-47), perform operations comprising:
obtaining building operational data from a plurality of building management systems (figs. 3, 4 – Various types of data are retrieved from various sources.; ¶ 27 – “In an example embodiment, a method for providing virtual energy audits may include steps for retrieving, by a processor of an analytics server, weather data corresponding to at least one target building from a weather server coupled to the analytics server, receiving, by the processor, utility energy usage data corresponding to the at least one target building, obtaining a set of building-specific characteristic data corresponding to the at least one target building, combining the weather data, utility energy usage data, and building-specific characteristic data into an energy audit dataset, determining a plurality of building markers for the at least one target building based on the energy audit dataset, generating prognostics data based on the energy audit dataset and building markers, the prognostics data including energy conservation recommendations and estimated impacts of implementing the energy conservation recommendations, generating building efficiency diagnostics based on the plurality of building markers, and sending the building efficiency diagnostics and the energy conservation recommendations to be displayed on a user interface of a client device coupled to the analytics server. The building efficiency diagnostics may include estimated heating/cooling system characteristics of the at least one target building.”; ¶ 30 – “In some embodiments, the at least one target building may include a plurality of target buildings. The building efficiency diagnostics may include a plurality of subsets of building efficiency diagnostics. The energy conservation recommendations may include a plurality of subsets of energy conservation recommendations. Each of the plurality of target buildings may correspond to a respectively different subset of building efficiency diagnostics of the plurality of subsets of building efficiency diagnostics. Each of the plurality of target buildings may correspond to a respectively different subset of energy conservation recommendations of the plurality of subsets of energy conservation recommendations.”);
providing, via a graphical user interface, a ranking of potential sustainable infrastructure projects based on scores for a plurality of potential sustainable infrastructure projects calculated based on at least one of utility information, climate data, building characteristics, or the building operational data, wherein the scores are generated at least in part on the building operational data (fig. 3 – Information is displayed on the client device.; ¶ 135 – “The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”; ¶ 27 – “In an example embodiment, a method for providing virtual energy audits may include steps for retrieving, by a processor of an analytics server, weather data corresponding to at least one target building from a weather server coupled to the analytics server, receiving, by the processor, utility energy usage data corresponding to the at least one target building, obtaining a set of building-specific characteristic data corresponding to the at least one target building, combining the weather data, utility energy usage data, and building-specific characteristic data into an energy audit dataset, determining a plurality of building markers for the at least one target building based on the energy audit dataset, generating prognostics data based on the energy audit dataset and building markers, the prognostics data including energy conservation recommendations and estimated impacts of implementing the energy conservation recommendations, generating building efficiency diagnostics based on the plurality of building markers, and sending the building efficiency diagnostics and the energy conservation recommendations to be displayed on a user interface of a client device coupled to the analytics server.”; ¶ 30 – “In some embodiments, the at least one target building may include a plurality of target buildings. The building efficiency diagnostics may include a plurality of subsets of building efficiency diagnostics. The energy conservation recommendations may include a plurality of subsets of energy conservation recommendations. Each of the plurality of target buildings may correspond to a respectively different subset of building efficiency diagnostics of the plurality of subsets of building efficiency diagnostics. Each of the plurality of target buildings may correspond to a respectively different subset of energy conservation recommendations of the plurality of subsets of energy conservation recommendations. The method may further include steps for quantifying a respective savings opportunity value for each of the plurality of target buildings to produce a plurality of savings opportunity values, the energy conservation recommendations including the plurality of savings opportunity values, generating an ordered list of the plurality of target buildings that is ordered based on the plurality of savings opportunity values, identifying a subset of target buildings of the plurality of target buildings associated with the highest savings opportunity values of the plurality of savings opportunity values, and sending the ordered list and the identified subset of target buildings to be displayed via the user interface of the client device.”; The quantified savings and energy amounts are examples of scores.).
[Claim 16] Abramson discloses wherein providing the scores further comprises accounting for retrofit constraints indicated by the building characteristics and comparing the utility information, the climate data, and the building characteristics (¶ 134 – “With additional metadata for, or an estimate of, window-to-wall ratio in the building, a parallel thermal resistance network analysis can be employed to determine the corresponding effective R-value of the windows in the building. Consequently, this analysis ultimately reveals the potential value of, and opportunity for, window replacement which is a common energy conservation measure (ECM) recommendation. For example, this analysis was applied to one real world buildings, and the resulting findings were then validated against an energy audit conducted by an on-site energy audit firm, to reveal that as-calculated low R-value buildings correspond well to those buildings which the company determined are in need of retro-commissioning or deep retrofit to achieve energy efficiency goals set by the building owner. The firm's on-site energy audit required engineers to access the building in-person, over several days per building and at a cost of thousands of dollars per building. The systems and methods disclosed herein are capable of diagnosing similar problems with the building within minutes and at a much reduced cost.”; ¶ 135 – “While the above examples have been provided in the context of analyzing a single building, it should be understood that multiple buildings may be similarly analyzed, with energy conservation prognostics and building efficiency diagnostics and corresponding reports being generated, stored, and/or displayed for each. As an example, a system for providing virtual energy audits (e.g., the system 300 of FIG. 3) may receive a list of target buildings from a client device, along with predefined building characteristics for each of the target buildings. The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 3-5 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Abramson et al. (US 2019/0347670), as applied to claims 1 and 12 above, in view of Volkman et al. (CA-2650178-A1).
[Claims 3, 14] Abramson discloses wherein:
the plurality of potential sustainable infrastructure projects comprise a first project for a first building and a second project for a second building (fig. 3 – Information is displayed on the client device.; ¶ 135 – “The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”; ¶ 27 – “In an example embodiment, a method for providing virtual energy audits may include steps for retrieving, by a processor of an analytics server, weather data corresponding to at least one target building from a weather server coupled to the analytics server, receiving, by the processor, utility energy usage data corresponding to the at least one target building, obtaining a set of building-specific characteristic data corresponding to the at least one target building, combining the weather data, utility energy usage data, and building-specific characteristic data into an energy audit dataset, determining a plurality of building markers for the at least one target building based on the energy audit dataset, generating prognostics data based on the energy audit dataset and building markers, the prognostics data including energy conservation recommendations and estimated impacts of implementing the energy conservation recommendations, generating building efficiency diagnostics based on the plurality of building markers, and sending the building efficiency diagnostics and the energy conservation recommendations to be displayed on a user interface of a client device coupled to the analytics server.”; ¶ 30 – “In some embodiments, the at least one target building may include a plurality of target buildings. The building efficiency diagnostics may include a plurality of subsets of building efficiency diagnostics. The energy conservation recommendations may include a plurality of subsets of energy conservation recommendations. Each of the plurality of target buildings may correspond to a respectively different subset of building efficiency diagnostics of the plurality of subsets of building efficiency diagnostics. Each of the plurality of target buildings may correspond to a respectively different subset of energy conservation recommendations of the plurality of subsets of energy conservation recommendations. The method may further include steps for quantifying a respective savings opportunity value for each of the plurality of target buildings to produce a plurality of savings opportunity values, the energy conservation recommendations including the plurality of savings opportunity values, generating an ordered list of the plurality of target buildings that is ordered based on the plurality of savings opportunity values, identifying a subset of target buildings of the plurality of target buildings associated with the highest savings opportunity values of the plurality of savings opportunity values, and sending the ordered list and the identified subset of target buildings to be displayed via the user interface of the client device.”; The quantified savings and energy amounts are examples of scores.);
the utility information comprises a first utility rate for the first building and a second utility rate for the second building (fig. 3 – Information is displayed on the client device.; ¶ 135 – “The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”; ¶ 27 – “In an example embodiment, a method for providing virtual energy audits may include steps for retrieving, by a processor of an analytics server, weather data corresponding to at least one target building from a weather server coupled to the analytics server, receiving, by the processor, utility energy usage data corresponding to the at least one target building, obtaining a set of building-specific characteristic data corresponding to the at least one target building, combining the weather data, utility energy usage data, and building-specific characteristic data into an energy audit dataset, determining a plurality of building markers for the at least one target building based on the energy audit dataset, generating prognostics data based on the energy audit dataset and building markers, the prognostics data including energy conservation recommendations and estimated impacts of implementing the energy conservation recommendations, generating building efficiency diagnostics based on the plurality of building markers, and sending the building efficiency diagnostics and the energy conservation recommendations to be displayed on a user interface of a client device coupled to the analytics server.”; ¶ 30 – “In some embodiments, the at least one target building may include a plurality of target buildings. The building efficiency diagnostics may include a plurality of subsets of building efficiency diagnostics. The energy conservation recommendations may include a plurality of subsets of energy conservation recommendations. Each of the plurality of target buildings may correspond to a respectively different subset of building efficiency diagnostics of the plurality of subsets of building efficiency diagnostics. Each of the plurality of target buildings may correspond to a respectively different subset of energy conservation recommendations of the plurality of subsets of energy conservation recommendations. The method may further include steps for quantifying a respective savings opportunity value for each of the plurality of target buildings to produce a plurality of savings opportunity values, the energy conservation recommendations including the plurality of savings opportunity values, generating an ordered list of the plurality of target buildings that is ordered based on the plurality of savings opportunity values, identifying a subset of target buildings of the plurality of target buildings associated with the highest savings opportunity values of the plurality of savings opportunity values, and sending the ordered list and the identified subset of target buildings to be displayed via the user interface of the client device.”; The quantified savings and energy amounts are examples of scores. Energy usage is an example of a utility rate. The energy usage of each building is evaluated.).
Abramson does not explicitly disclose that the step of scoring the potential sustainable infrastructure projects comprises improving a first score for the first project relative to a second score for the second project if the first utility rate is higher than the second utility rate. However, Volkman explains that utility data (including energy usage) may be obtained from the respective utility companies of various users for comparison purposes and in order to make appropriate recommendations for reducing a carbon footprint, energy usage, etc., as seen in the following excerpts:
[0029] The users l 20A-N may register for a service for providing actions to reduce a carbon footprint provided by the service provider 110. The users l 20A-N may provide personal information to the service provider 110, such as name, address, social security number, or generally any information that may be used to obtain energy use data from the utility providers 140. The users 120A-N may manually enter energy use information used to determine a carbon footprint of the users 120A-N. The energy use information may be specific to the user. For example, the users 120A-N may enter information about the square footage of their home, the number of bedrooms, the vehicles they own, the vehicle usage, the types of appliances in the home, the sources of energy to the home, such as electricity, natural gas, propane, coal, solar panels, and any other information. Depending on an implementation, energy use information may also be gathered in other ways, such as directly via the utility providers 140. For example, the utility network control systems 330 may provide an application programming interface (API) to allow the service provider 110 to receive energy use information from the utility providers 140. The service provider 110 may use the information provided by the users 120A-N and/or other sources, for example the transaction data providers 140 and reference data providers 145, to determine energy use and a carbon footprint of the users 120A-N.
[0032] Since the use of a particular type of energy may vary depending upon the geographic location of the users 120A-N, the energy use of the user A 120A may be compared against the energy use of users 120B-N located within the same geographic area as the user A 120A. Alternatively or in addition, the users 120BN may be located in a geographic area with the same climate as the user A 120A, or within an area where the electric energy providers use the same fuel mix to provide electricity, such as a mix of renewable energy, nuclear energy, natural gas, coal, and/or generally any type of energy capable of producing electricity. The carbon footprint data may also include a trend analysis. The trend analysis may display the historical emissions data of the users 120A-N and may indicate whether the users 120A-N are increasing or decreasing their emissions.
[0050] At block 440 the service provider 110 may determine carbon reducing actions relating to the energy use/emission data of the user A 120A. The actions may be tailored to each user such as based on actual energy uses of the user. The
actions may be prioritized in accordance with a relative impact that the action has on a carbon footprint of the user. Actions that provide a greater impact on the carbon footprint may be emphasized. Alternatively and/or additionally, the service provider 110 may compare the emissions data of the user A 120A to the emissions data of the users 120B-N. The set of users 120B-N may be the users 1208-N located in the same geographic region as the user A 120A, the users 1208-N located in a similar climate as the user A 120A, the users 120B-N with similar size/age homes as the user A 120A, or generally any segment of the users 120B-N which may indicate the relative emissions of the user A 120A. The service provider 110 may determine relative emissions of the user A 120A for each energy use type. The service provider 110 may provide recommended actions to the user A 120A geared towards the energy use types where the user A 120A is generating relatively large amounts of carbon emissions.
[0053] At block 455 the service provider 110 may display the emission data and the recommended actions to the user A 120A. The emission data may be displayed in a bar graph, a pie chart, or generally any method of displaying the data so as to convey the carbon emissions of the user A 120A. The emissions data of the user A 120A may be compared against users 1208-N with similar energy needs of the user A 120A in order to provide a relative perspective of the energy consumption of the user A 120A.
[0057] At block 520 the service provider 110 may determine whether the user A 120A should be recommended actions to reduce their emissions due to electricity usage. If the emissions due to the electricity use of the user A 120A are higher than the emissions due to the electricity use of similar users 120B-N, the system may move to block 525. If the emissions due to the electricity use of the user A 120A are less than or equal to emissions due to the electricity use of similar users 120B-N, the system 200 may move to block 530.
(0058] At block 525 the service provider 110 may determine a carbon reducing action related to electricity use. For example, the service provider 110 may recommend that the user A 120A replace energy consuming appliances, such as a washer/dryer, with more efficient appliances. Alternatively or in addition, the service provider 110 may recommend the user A 120A unplug unneeded appliances, such as a third refrigerator. (Emphasis added)
The home of each of Volkman’s users is analogous to each of the buildings in Abramson. Volkman looks are relative usage to identify areas in greater need of being addressed to reduce carbon emissions, including those related to electricity use (which implies that higher electricity use is associated with a larger carbon footprint and greater carbon emissions). When a user uses more energy than comparable users, as gleaned from utility data for each of multiple users, then a recommendation is made for that user to reduce carbon emissions, electricity use, etc. In effect, the claim limitation “scoring the potential sustainable infrastructure projects comprises improving a first score for the first project relative to a second score for the second project if the first utility rate is higher than the second utility rate” adds a consideration of greater criticality when one utility rate (such as energy usage) is relatively higher than another utility rate. The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Abramson such that the step of scoring the potential sustainable infrastructure projects comprises improving a first score for the first project relative to a second score for the second project if the first utility rate is higher than the second utility rate in order to allow for areas of greater criticality (such as above average carbon footprints, energy usage, etc.) to be emphasized when ranking projects by greatest impact, urgency, etc., thereby allowing for projects deemed to be of greater importance to be given higher priority relative to less critical projects.
[Claims 4, 15] Abramson discloses wherein:
the plurality of potential sustainable infrastructure projects comprise a first project for a first building and a second project for a second building (fig. 3 – Information is displayed on the client device.; ¶ 135 – “The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”; ¶ 27 – “In an example embodiment, a method for providing virtual energy audits may include steps for retrieving, by a processor of an analytics server, weather data corresponding to at least one target building from a weather server coupled to the analytics server, receiving, by the processor, utility energy usage data corresponding to the at least one target building, obtaining a set of building-specific characteristic data corresponding to the at least one target building, combining the weather data, utility energy usage data, and building-specific characteristic data into an energy audit dataset, determining a plurality of building markers for the at least one target building based on the energy audit dataset, generating prognostics data based on the energy audit dataset and building markers, the prognostics data including energy conservation recommendations and estimated impacts of implementing the energy conservation recommendations, generating building efficiency diagnostics based on the plurality of building markers, and sending the building efficiency diagnostics and the energy conservation recommendations to be displayed on a user interface of a client device coupled to the analytics server.”; ¶ 30 – “In some embodiments, the at least one target building may include a plurality of target buildings. The building efficiency diagnostics may include a plurality of subsets of building efficiency diagnostics. The energy conservation recommendations may include a plurality of subsets of energy conservation recommendations. Each of the plurality of target buildings may correspond to a respectively different subset of building efficiency diagnostics of the plurality of subsets of building efficiency diagnostics. Each of the plurality of target buildings may correspond to a respectively different subset of energy conservation recommendations of the plurality of subsets of energy conservation recommendations. The method may further include steps for quantifying a respective savings opportunity value for each of the plurality of target buildings to produce a plurality of savings opportunity values, the energy conservation recommendations including the plurality of savings opportunity values, generating an ordered list of the plurality of target buildings that is ordered based on the plurality of savings opportunity values, identifying a subset of target buildings of the plurality of target buildings associated with the highest savings opportunity values of the plurality of savings opportunity values, and sending the ordered list and the identified subset of target buildings to be displayed via the user interface of the client device.”; The quantified savings and energy amounts are examples of scores.).
Abramson does not explicitly disclose the following:
the utility information comprises a first grid emissions rate associated with energy generated for provision to the first building and a second grid emissions rate associated with energy generated for provision to the second building; and
scoring the potential sustainable infrastructure projects comprises improving a first score for the first project relative to a second score for the second project if the first grid emissions rate is higher than the second grid emissions rate.
However, Volkman explains that utility data (including energy usage) may be obtained from the respective utility companies of various users for comparison purposes and in order to make appropriate recommendations for reducing a carbon footprint, energy usage, etc., as seen in the following excerpts:
[0029] The users l 20A-N may register for a service for providing actions to reduce a carbon footprint provided by the service provider 110. The users l 20A-N may provide personal information to the service provider 110, such as name, address, social security number, or generally any information that may be used to obtain energy use data from the utility providers 140. The users 120A-N may manually enter energy use information used to determine a carbon footprint of the users 120A-N. The energy use information may be specific to the user. For example, the users 120A-N may enter information about the square footage of their home, the number of bedrooms, the vehicles they own, the vehicle usage, the types of appliances in the home, the sources of energy to the home, such as electricity, natural gas, propane, coal, solar panels, and any other information. Depending on an implementation, energy use information may also be gathered in other ways, such as directly via the utility providers 140. For example, the utility network control systems 330 may provide an application programming interface (API) to allow the service provider 110 to receive energy use information from the utility providers 140. The service provider 110 may use the information provided by the users 120A-N and/or other sources, for example the transaction data providers 140 and reference data providers 145, to determine energy use and a carbon footprint of the users 120A-N.
[0032] Since the use of a particular type of energy may vary depending upon the geographic location of the users 120A-N, the energy use of the user A 120A may be compared against the energy use of users 120B-N located within the same geographic area as the user A 120A. Alternatively or in addition, the users 120BN may be located in a geographic area with the same climate as the user A 120A, or within an area where the electric energy providers use the same fuel mix to provide electricity, such as a mix of renewable energy, nuclear energy, natural gas, coal, and/or generally any type of energy capable of producing electricity. The carbon footprint data may also include a trend analysis. The trend analysis may display the historical emissions data of the users 120A-N and may indicate whether the users 120A-N are increasing or decreasing their emissions.
[0050] At block 440 the service provider 110 may determine carbon reducing actions relating to the energy use/emission data of the user A 120A. The actions may be tailored to each user such as based on actual energy uses of the user. The
actions may be prioritized in accordance with a relative impact that the action has on a carbon footprint of the user. Actions that provide a greater impact on the carbon footprint may be emphasized. Alternatively and/or additionally, the service provider 110 may compare the emissions data of the user A 120A to the emissions data of the users 120B-N. The set of users 120B-N may be the users 1208-N located in the same geographic region as the user A 120A, the users 1208-N located in a similar climate as the user A 120A, the users 120B-N with similar size/age homes as the user A 120A, or generally any segment of the users 120B-N which may indicate the relative emissions of the user A 120A. The service provider 110 may determine relative emissions of the user A 120A for each energy use type. The service provider 110 may provide recommended actions to the user A 120A geared towards the energy use types where the user A 120A is generating relatively large amounts of carbon emissions.
[0053] At block 455 the service provider 110 may display the emission data and the recommended actions to the user A 120A. The emission data may be displayed in a bar graph, a pie chart, or generally any method of displaying the data so as to convey the carbon emissions of the user A 120A. The emissions data of the user A 120A may be compared against users 1208-N with similar energy needs of the user A 120A in order to provide a relative perspective of the energy consumption of the user A 120A.
[0057] At block 520 the service provider 110 may determine whether the user A 120A should be recommended actions to reduce their emissions due to electricity usage. If the emissions due to the electricity use of the user A 120A are higher than the emissions due to the electricity use of similar users 120B-N, the system may move to block 525. If the emissions due to the electricity use of the user A 120A are less than or equal to emissions due to the electricity use of similar users 120B-N, the system 200 may move to block 530.
(0058] At block 525 the service provider 110 may determine a carbon reducing action related to electricity use. For example, the service provider 110 may recommend that the user A 120A replace energy consuming appliances, such as a washer/dryer, with more efficient appliances. Alternatively or in addition, the service provider 110 may recommend the user A 120A unplug unneeded appliances, such as a third refrigerator. (Emphasis added)
The home of each of Volkman’s users is analogous to each of the buildings in Abramson. Volkman looks are relative usage to identify areas in greater need of being addressed to reduce carbon emissions, including those related to electricity use (which implies that higher electricity use is associated with a larger carbon footprint and greater carbon emissions). When a user uses more energy than comparable users, as gleaned from utility data for each of multiple users, then a recommendation is made for that user to reduce carbon emissions, electricity use, etc. In effect, the claim limitation “scoring the potential sustainable infrastructure projects comprises improving a first score for the first project relative to a second score for the second project if the first utility rate is higher than the second utility rate” adds a consideration of greater criticality when one utility rate (such as energy usage) is relatively higher than another utility rate. The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Abramson to incorporate the following:
the utility information comprises a first grid emissions rate associated with energy generated for provision to the first building and a second grid emissions rate associated with energy generated for provision to the second building; and
scoring the potential sustainable infrastructure projects comprises improving a first score for the first project relative to a second score for the second project if the first grid emissions rate is higher than the second grid emissions rate
in order to allow for areas of greater criticality (such as above average carbon footprints, energy usage, etc.) to be emphasized when ranking projects by greatest impact, urgency, etc., thereby allowing for projects deemed to be of greater importance to be given higher priority relative to less critical projects.
[Claim 5] Abramson does not explicitly disclose wherein the utility information indicates one or more of incentive program information, demand response program information, frequency regulation program information, demand charge information, and energy buy-back rates.
Volkman offers users awards and incentives for exchanging surplus carbon emissions credits, which are obtained by decreasing emissions (Volkman: ¶¶ 51-52). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Abramson wherein the utility information indicates one or more of incentive program information, demand response program information, frequency regulation program information, demand charge information, and energy buy-back rates in order to encourage users to perform desirable behavior, such as reducing emissions.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Abramson et al. (US 2019/0347670), as applied to claim 1 above, in view of Yendigeri et al. (US 2023/0141408).
[Claim 6] Abramson does not explicitly disclose wherein providing the scores is further based on historical pitch success rates for infrastructure projects. However, like Abramson, Yendigeri makes client solution recommendations. Additionally, Yendigeri evaluates historical success rates in order to continue to weigh recommendations based on confidence scores (Yendigeri: ¶¶ 69-72). For example, Yendigeri explains:
[0071] In some implementations, processing the historical project data and the client data, with the one or more machine learning models, to generate the recommendations for the problem of the client and the confidence scores for the recommendations includes applying weights to the recommendations based on historical success rates associated with the recommendations, and generating the confidence scores for the recommendations based on applying the weights to the recommendations. In some implementations, each of the recommendations includes a template structure, one or more content sections for the template structure, a design for the template structure, and one or more templates generated by subject matter experts.
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Abramson wherein providing the scores is further based on historical pitch success rates for infrastructure projects in order to glean past knowledge that is useful in modifying assumptions for future predictions of project recommendation success, thereby continuously improving the accuracy of the predictive recommendation models for greater satisfaction related to the recommended infrastructure projects.
Claims 11, 13, 17, 18, and 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Abramson et al. (US 2019/0347670), as applied to claims 1 and 12 above, in view of Takahashi et al. (US 2011/0161131).
[Claim 11] Abramson alludes to cost in reference to return-on-investment (ROI) estimates and future economic return (Abramson: ¶¶ 5-6); however, Abramson does not explicitly disclose wherein providing the scores further comprises determining prices for the plurality of potential sustainable infrastructure projects. Takahashi evaluates cost for energy saving projects and compares total installation cost of energy saving equipment to a total estimated budget to ensure that the installation cost does not exceed the budget (Takahashi: ¶ 20). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Abramson wherein providing the scores further comprises determining prices for the plurality of potential sustainable infrastructure projects so that the economic feasibility of potential sustainable infrastructure projects may be taken into account when assessing which projects can actually be carried out within any budgetary constraints.
[Claim 13] Abramson discloses wherein the operations further comprise planning to initiate at least some of the potential sustainable infrastructure projects in an order based on the ranking (fig. 3 – Information is displayed on the client device.; ¶ 135 – “The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”; ¶ 27 – “In an example embodiment, a method for providing virtual energy audits may include steps for retrieving, by a processor of an analytics server, weather data corresponding to at least one target building from a weather server coupled to the analytics server, receiving, by the processor, utility energy usage data corresponding to the at least one target building, obtaining a set of building-specific characteristic data corresponding to the at least one target building, combining the weather data, utility energy usage data, and building-specific characteristic data into an energy audit dataset, determining a plurality of building markers for the at least one target building based on the energy audit dataset, generating prognostics data based on the energy audit dataset and building markers, the prognostics data including energy conservation recommendations and estimated impacts of implementing the energy conservation recommendations, generating building efficiency diagnostics based on the plurality of building markers, and sending the building efficiency diagnostics and the energy conservation recommendations to be displayed on a user interface of a client device coupled to the analytics server.”; ¶ 30 – “In some embodiments, the at least one target building may include a plurality of target buildings. The building efficiency diagnostics may include a plurality of subsets of building efficiency diagnostics. The energy conservation recommendations may include a plurality of subsets of energy conservation recommendations. Each of the plurality of target buildings may correspond to a respectively different subset of building efficiency diagnostics of the plurality of subsets of building efficiency diagnostics. Each of the plurality of target buildings may correspond to a respectively different subset of energy conservation recommendations of the plurality of subsets of energy conservation recommendations. The method may further include steps for quantifying a respective savings opportunity value for each of the plurality of target buildings to produce a plurality of savings opportunity values, the energy conservation recommendations including the plurality of savings opportunity values, generating an ordered list of the plurality of target buildings that is ordered based on the plurality of savings opportunity values, identifying a subset of target buildings of the plurality of target buildings associated with the highest savings opportunity values of the plurality of savings opportunity values, and sending the ordered list and the identified subset of target buildings to be displayed via the user interface of the client device.”; The quantified savings and energy amounts are examples of scores.).
Abramson does not explicitly disclose wherein the operations further comprise actively initiating at least some of the potential sustainable infrastructure projects in an order based on the ranking. However, Abramson states, “For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.” (Abramson: ¶ 135) In other words, Abramson makes recommendations that are intended to be implemented to realize desired benefits (such as saving money and/or energy).
Additionally, Takahashi describes the implementation of energy saving solutions at each of multiple bases in order of priority and explicitly states the following in paragraph 19:
[0019] (1) The present invention provides an energy saving assistance system that includes a program that runs on at least one computer, and creates an energy saving plan for defining time when to introduce an energy saving solution implemented by installing an energy saving equipment. The program includes: a user setting section for setting as users need a target reduction of energy consumption through all of plural bases for every year and a priority order of selecting from the plural bases a base where an energy saving solution is preferentially carried out; an elemental effect calculation section for calculating an energy effect of every energy saving solution for a base of interest among the bases where the energy saving solution is preferentially carried out in the priority order, with reference to a data base storing equipment information before installing the energy saving equipment for the base of interest and a data base storing specifications of the energy saving equipment for the base of interest, and this calculation of the energy saving effect of every energy saving solution being carried out for every base of the bases where the energy saving solution is preferentially carried out in the priority order, and outputting the calculated energy saving effect of every energy saving solution for every base in a form of an elemental effect table; a consolidation section for creating the energy saving plan in accordance with the users need, using the elemental effect table, and representing this energy saving plan to the user; and for every action year, the consolidation section adopts an energy saving solution set in the elemental effect table as the energy saving solution to be introduced in an action year of interest in the priority order of base selection for the energy saving solution, so that total energy saving effect due to the adopted energy saving solution can clear a target reduction to be achieved in a next year after a year when the energy saving solution is carried out, and represents to the user an energy saving plan that specifies an energy saving equipment to be installed and a year when to install the equipment. (Emphasis added)
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Abramson wherein the operations further comprise actively initiating at least some of the potential sustainable infrastructure projects in an order based on the ranking in an order indicated by the ranking in order to facilitate the active realization of the desired benefits (including money and/or energy savings).
[Claim 17] Abramson alludes to cost in reference to return-on-investment (ROI) estimates and future economic return (Abramson: ¶¶ 5-6); however, Abramson does not explicitly disclose that the operations further comprise predicting savings expected to result from the plurality of potential sustainable infrastructure projects, determining prices for the plurality of potential sustainable infrastructure projects, and comparing the savings with the prices. Takahashi evaluates cost for energy saving projects and compares total installation cost of energy saving equipment to a total estimated budget to ensure that the installation cost does not exceed the budget (Takahashi: ¶ 20). Takahashi also determines a cost recovery period for an installation cost based on an energy saving cost divided by an annual cost reduction due to the energy saving solution (Takahashi: ¶ 128). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Abramson such that the operations further comprise predicting savings expected to result from the plurality of potential sustainable infrastructure projects, determining prices for the plurality of potential sustainable infrastructure projects, and comparing the savings with the prices so that the economic feasibility and return on investment of potential sustainable infrastructure projects may be taken into account when assessing which projects can actually be carried out within any budgetary constraints and are worth the effort to carry out.
[Claim 18] Abramson discloses a method of executing sustainable infrastructure projects (abstract – “The energy conservation prognostics may include one or more energy conservation measure recommendations and corresponding predicted cost/energy savings.”), comprising:
ranking the sustainable infrastructure projects by scoring the sustainable infrastructure projects based on utility information, climate data, building characteristics, and building operational data associated with the sustainable infrastructure projects (fig. 3 – Information is displayed on the client device.; ¶ 135 – “The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”; ¶ 27 – “In an example embodiment, a method for providing virtual energy audits may include steps for retrieving, by a processor of an analytics server, weather data corresponding to at least one target building from a weather server coupled to the analytics server, receiving, by the processor, utility energy usage data corresponding to the at least one target building, obtaining a set of building-specific characteristic data corresponding to the at least one target building, combining the weather data, utility energy usage data, and building-specific characteristic data into an energy audit dataset, determining a plurality of building markers for the at least one target building based on the energy audit dataset, generating prognostics data based on the energy audit dataset and building markers, the prognostics data including energy conservation recommendations and estimated impacts of implementing the energy conservation recommendations, generating building efficiency diagnostics based on the plurality of building markers, and sending the building efficiency diagnostics and the energy conservation recommendations to be displayed on a user interface of a client device coupled to the analytics server.”; ¶ 30 – “In some embodiments, the at least one target building may include a plurality of target buildings. The building efficiency diagnostics may include a plurality of subsets of building efficiency diagnostics. The energy conservation recommendations may include a plurality of subsets of energy conservation recommendations. Each of the plurality of target buildings may correspond to a respectively different subset of building efficiency diagnostics of the plurality of subsets of building efficiency diagnostics. Each of the plurality of target buildings may correspond to a respectively different subset of energy conservation recommendations of the plurality of subsets of energy conservation recommendations. The method may further include steps for quantifying a respective savings opportunity value for each of the plurality of target buildings to produce a plurality of savings opportunity values, the energy conservation recommendations including the plurality of savings opportunity values, generating an ordered list of the plurality of target buildings that is ordered based on the plurality of savings opportunity values, identifying a subset of target buildings of the plurality of target buildings associated with the highest savings opportunity values of the plurality of savings opportunity values, and sending the ordered list and the identified subset of target buildings to be displayed via the user interface of the client device.”; The quantified savings and energy amounts are examples of scores.).
Abramson discloses recommendations to install building equipment or modify at least one building to execute the sustainable infrastructure projects in an order indicated by the ranking (fig. 3 – Information is displayed on the client device.; ¶ 135 – “The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”; ¶ 27 – “In an example embodiment, a method for providing virtual energy audits may include steps for retrieving, by a processor of an analytics server, weather data corresponding to at least one target building from a weather server coupled to the analytics server, receiving, by the processor, utility energy usage data corresponding to the at least one target building, obtaining a set of building-specific characteristic data corresponding to the at least one target building, combining the weather data, utility energy usage data, and building-specific characteristic data into an energy audit dataset, determining a plurality of building markers for the at least one target building based on the energy audit dataset, generating prognostics data based on the energy audit dataset and building markers, the prognostics data including energy conservation recommendations and estimated impacts of implementing the energy conservation recommendations, generating building efficiency diagnostics based on the plurality of building markers, and sending the building efficiency diagnostics and the energy conservation recommendations to be displayed on a user interface of a client device coupled to the analytics server.”; ¶ 30 – “In some embodiments, the at least one target building may include a plurality of target buildings. The building efficiency diagnostics may include a plurality of subsets of building efficiency diagnostics. The energy conservation recommendations may include a plurality of subsets of energy conservation recommendations. Each of the plurality of target buildings may correspond to a respectively different subset of building efficiency diagnostics of the plurality of subsets of building efficiency diagnostics. Each of the plurality of target buildings may correspond to a respectively different subset of energy conservation recommendations of the plurality of subsets of energy conservation recommendations. The method may further include steps for quantifying a respective savings opportunity value for each of the plurality of target buildings to produce a plurality of savings opportunity values, the energy conservation recommendations including the plurality of savings opportunity values, generating an ordered list of the plurality of target buildings that is ordered based on the plurality of savings opportunity values, identifying a subset of target buildings of the plurality of target buildings associated with the highest savings opportunity values of the plurality of savings opportunity values, and sending the ordered list and the identified subset of target buildings to be displayed via the user interface of the client device.”; The quantified savings and energy amounts are examples of scores.).
Abramson does not explicitly disclose actively installing building equipment or modifying at least one building to execute the sustainable infrastructure projects in an order indicated by the ranking. However, Abramson states, “For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.” (Abramson: ¶ 135) In other words, Abramson makes recommendations that are intended to be implemented to realize desired benefits (such as saving money and/or energy).
Additionally, Takahashi describes the implementation of energy saving solutions at each of multiple bases in order of priority and explicitly states the following in paragraph 19:
[0019] (1) The present invention provides an energy saving assistance system that includes a program that runs on at least one computer, and creates an energy saving plan for defining time when to introduce an energy saving solution implemented by installing an energy saving equipment. The program includes: a user setting section for setting as users need a target reduction of energy consumption through all of plural bases for every year and a priority order of selecting from the plural bases a base where an energy saving solution is preferentially carried out; an elemental effect calculation section for calculating an energy effect of every energy saving solution for a base of interest among the bases where the energy saving solution is preferentially carried out in the priority order, with reference to a data base storing equipment information before installing the energy saving equipment for the base of interest and a data base storing specifications of the energy saving equipment for the base of interest, and this calculation of the energy saving effect of every energy saving solution being carried out for every base of the bases where the energy saving solution is preferentially carried out in the priority order, and outputting the calculated energy saving effect of every energy saving solution for every base in a form of an elemental effect table; a consolidation section for creating the energy saving plan in accordance with the users need, using the elemental effect table, and representing this energy saving plan to the user; and for every action year, the consolidation section adopts an energy saving solution set in the elemental effect table as the energy saving solution to be introduced in an action year of interest in the priority order of base selection for the energy saving solution, so that total energy saving effect due to the adopted energy saving solution can clear a target reduction to be achieved in a next year after a year when the energy saving solution is carried out, and represents to the user an energy saving plan that specifies an energy saving equipment to be installed and a year when to install the equipment. (Emphasis added)
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Abramson to actively install building equipment or modify at least one building to execute the sustainable infrastructure projects in an order indicated by the ranking in order to facilitate the active realization of the desired benefits (including money and/or energy savings).
[Claim 20] Abramson discloses wherein scoring the sustainable infrastructure projects is based on retrofit constraints indicated by the building characteristics or the building operational data (¶ 134 – “With additional metadata for, or an estimate of, window-to-wall ratio in the building, a parallel thermal resistance network analysis can be employed to determine the corresponding effective R-value of the windows in the building. Consequently, this analysis ultimately reveals the potential value of, and opportunity for, window replacement which is a common energy conservation measure (ECM) recommendation. For example, this analysis was applied to one real world buildings, and the resulting findings were then validated against an energy audit conducted by an on-site energy audit firm, to reveal that as-calculated low R-value buildings correspond well to those buildings which the company determined are in need of retro-commissioning or deep retrofit to achieve energy efficiency goals set by the building owner. The firm's on-site energy audit required engineers to access the building in-person, over several days per building and at a cost of thousands of dollars per building. The systems and methods disclosed herein are capable of diagnosing similar problems with the building within minutes and at a much reduced cost.”; ¶ 135 – “While the above examples have been provided in the context of analyzing a single building, it should be understood that multiple buildings may be similarly analyzed, with energy conservation prognostics and building efficiency diagnostics and corresponding reports being generated, stored, and/or displayed for each. As an example, a system for providing virtual energy audits (e.g., the system 300 of FIG. 3) may receive a list of target buildings from a client device, along with predefined building characteristics for each of the target buildings. The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”).
[Claim 21] Abramson discloses providing, via a graphical user interface, the ranking of the sustainable infrastructure projects based on the scoring (fig. 3 – Information is displayed on the client device.; ¶ 135 – “The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”; ¶ 27 – “In an example embodiment, a method for providing virtual energy audits may include steps for retrieving, by a processor of an analytics server, weather data corresponding to at least one target building from a weather server coupled to the analytics server, receiving, by the processor, utility energy usage data corresponding to the at least one target building, obtaining a set of building-specific characteristic data corresponding to the at least one target building, combining the weather data, utility energy usage data, and building-specific characteristic data into an energy audit dataset, determining a plurality of building markers for the at least one target building based on the energy audit dataset, generating prognostics data based on the energy audit dataset and building markers, the prognostics data including energy conservation recommendations and estimated impacts of implementing the energy conservation recommendations, generating building efficiency diagnostics based on the plurality of building markers, and sending the building efficiency diagnostics and the energy conservation recommendations to be displayed on a user interface of a client device coupled to the analytics server.”; ¶ 30 – “In some embodiments, the at least one target building may include a plurality of target buildings. The building efficiency diagnostics may include a plurality of subsets of building efficiency diagnostics. The energy conservation recommendations may include a plurality of subsets of energy conservation recommendations. Each of the plurality of target buildings may correspond to a respectively different subset of building efficiency diagnostics of the plurality of subsets of building efficiency diagnostics. Each of the plurality of target buildings may correspond to a respectively different subset of energy conservation recommendations of the plurality of subsets of energy conservation recommendations. The method may further include steps for quantifying a respective savings opportunity value for each of the plurality of target buildings to produce a plurality of savings opportunity values, the energy conservation recommendations including the plurality of savings opportunity values, generating an ordered list of the plurality of target buildings that is ordered based on the plurality of savings opportunity values, identifying a subset of target buildings of the plurality of target buildings associated with the highest savings opportunity values of the plurality of savings opportunity values, and sending the ordered list and the identified subset of target buildings to be displayed via the user interface of the client device.”; The quantified savings and energy amounts are examples of scores.).
[Claim 22] Abramson discloses recommendations to install building equipment or modify at least one of the buildings to execute the potential sustainable infrastructure projects in an order indicated by the ranking (fig. 3 – Information is displayed on the client device.; ¶ 135 – “The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”; ¶ 27 – “In an example embodiment, a method for providing virtual energy audits may include steps for retrieving, by a processor of an analytics server, weather data corresponding to at least one target building from a weather server coupled to the analytics server, receiving, by the processor, utility energy usage data corresponding to the at least one target building, obtaining a set of building-specific characteristic data corresponding to the at least one target building, combining the weather data, utility energy usage data, and building-specific characteristic data into an energy audit dataset, determining a plurality of building markers for the at least one target building based on the energy audit dataset, generating prognostics data based on the energy audit dataset and building markers, the prognostics data including energy conservation recommendations and estimated impacts of implementing the energy conservation recommendations, generating building efficiency diagnostics based on the plurality of building markers, and sending the building efficiency diagnostics and the energy conservation recommendations to be displayed on a user interface of a client device coupled to the analytics server.”; ¶ 30 – “In some embodiments, the at least one target building may include a plurality of target buildings. The building efficiency diagnostics may include a plurality of subsets of building efficiency diagnostics. The energy conservation recommendations may include a plurality of subsets of energy conservation recommendations. Each of the plurality of target buildings may correspond to a respectively different subset of building efficiency diagnostics of the plurality of subsets of building efficiency diagnostics. Each of the plurality of target buildings may correspond to a respectively different subset of energy conservation recommendations of the plurality of subsets of energy conservation recommendations. The method may further include steps for quantifying a respective savings opportunity value for each of the plurality of target buildings to produce a plurality of savings opportunity values, the energy conservation recommendations including the plurality of savings opportunity values, generating an ordered list of the plurality of target buildings that is ordered based on the plurality of savings opportunity values, identifying a subset of target buildings of the plurality of target buildings associated with the highest savings opportunity values of the plurality of savings opportunity values, and sending the ordered list and the identified subset of target buildings to be displayed via the user interface of the client device.”; The quantified savings and energy amounts are examples of scores.).
Abramson does not explicitly disclose actively installing building equipment or modifying at least one of the buildings to execute the potential sustainable infrastructure projects in an order indicated by the ranking. However, Abramson states, “For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.” (Abramson: ¶ 135) In other words, Abramson makes recommendations that are intended to be implemented to realize desired benefits (such as saving money and/or energy).
Additionally, Takahashi describes the implementation of energy saving solutions at each of multiple bases in order of priority and explicitly states the following in paragraph 19:
[0019] (1) The present invention provides an energy saving assistance system that includes a program that runs on at least one computer, and creates an energy saving plan for defining time when to introduce an energy saving solution implemented by installing an energy saving equipment. The program includes: a user setting section for setting as users need a target reduction of energy consumption through all of plural bases for every year and a priority order of selecting from the plural bases a base where an energy saving solution is preferentially carried out; an elemental effect calculation section for calculating an energy effect of every energy saving solution for a base of interest among the bases where the energy saving solution is preferentially carried out in the priority order, with reference to a data base storing equipment information before installing the energy saving equipment for the base of interest and a data base storing specifications of the energy saving equipment for the base of interest, and this calculation of the energy saving effect of every energy saving solution being carried out for every base of the bases where the energy saving solution is preferentially carried out in the priority order, and outputting the calculated energy saving effect of every energy saving solution for every base in a form of an elemental effect table; a consolidation section for creating the energy saving plan in accordance with the users need, using the elemental effect table, and representing this energy saving plan to the user; and for every action year, the consolidation section adopts an energy saving solution set in the elemental effect table as the energy saving solution to be introduced in an action year of interest in the priority order of base selection for the energy saving solution, so that total energy saving effect due to the adopted energy saving solution can clear a target reduction to be achieved in a next year after a year when the energy saving solution is carried out, and represents to the user an energy saving plan that specifies an energy saving equipment to be installed and a year when to install the equipment. (Emphasis added)
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Abramson to actively install building equipment or modifying at least one of the buildings to execute the potential sustainable infrastructure projects in an order indicated by the ranking in order to facilitate the active realization of the desired benefits (including money and/or energy savings).
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Abramson et al. (US 2019/0347670) in view of Takahashi et al. (US 2011/0161131), as applied to claim 18 above, in view of Volkman et al. (CA-2650178-A1).
[Claim 19] Abramson discloses wherein:
the sustainable infrastructure projects comprise a first project for a first building and a second project for a second building (fig. 3 – Information is displayed on the client device.; ¶ 135 – “The system may generate energy conservation prognostics and building efficiency diagnostics for each of the target buildings based on corresponding weather data and energy usage data in combination with the predefined building characteristics for each building. The system may then rank the target buildings according to one or more predefined factors. For example, the system may rank the target buildings according to how much money and/or energy could be saved by implementing recommended operational adjustments and/or retrofits (e.g., ECM recommendations) based on the energy conservation prognostics. A predefined number of target buildings for which the most money/energy is estimated to be saved if such recommendations are implemented may then be sent to the client device. In this way, an individual or corporation owning many buildings may quickly and cheaply identify which of their buildings should be adjusted or retrofitted with the highest priority, compared to conventional auditing methods, which may require time consuming and expensive individual walkthroughs of each of the buildings being audited.”; ¶ 27 – “In an example embodiment, a method for providing virtual energy audits may include steps for retrieving, by a processor of an analytics server, weather data corresponding to at least one target building from a weather server coupled to the analytics server, receiving, by the processor, utility energy usage data corresponding to the at least one target building, obtaining a set of building-specific characteristic data corresponding to the at least one target building, combining the weather data, utility energy usage data, and building-specific characteristic data into an energy audit dataset, determining a plurality of building markers for the at least one target building based on the energy audit dataset, generating prognostics data based on the energy audit dataset and building markers, the prognostics data including energy conservation recommendations and estimated impacts of implementing the energy conservation recommendations, generating building efficiency diagnostics based on the plurality of building markers, and sending the building efficiency diagnostics and the energy conservation recommendations to be displayed on a user interface of a client device coupled to the analytics server.”; ¶ 30 – “In some embodiments, the at least one target building may include a plurality of target buildings. The building efficiency diagnostics may include a plurality of subsets of building efficiency diagnostics. The energy conservation recommendations may include a plurality of subsets of energy conservation recommendations. Each of the plurality of target buildings may correspond to a respectively different subset of building efficiency diagnostics of the plurality of subsets of building efficiency diagnostics. Each of the plurality of target buildings may correspond to a respectively different subset of energy conservation recommendations of the plurality of subsets of energy conservation recommendations. The method may further include steps for quantifying a respective savings opportunity value for each of the plurality of target buildings to produce a plurality of savings opportunity values, the energy conservation recommendations including the plurality of savings opportunity values, generating an ordered list of the plurality of target buildings that is ordered based on the plurality of savings opportunity values, identifying a subset of target buildings of the plurality of target buildings associated with the highest savings opportunity values of the plurality of savings opportunity values, and sending the ordered list and the identified subset of target buildings to be displayed via the user interface of the client device.”; The quantified savings and energy amounts are examples of scores.).
Abramson does not explicitly disclose the following:
the utility information comprises a first grid emissions rate associated with energy generated for provision to the first building and a second grid emissions rate associated with energy generated for provision to the second building; and
scoring the sustainable infrastructure projects comprises improving a first score for the first project relative to a second score for the second project if the first grid emissions rate is higher than the second grid emissions rate.
However, Volkman explains that utility data (including energy usage) may be obtained from the respective utility companies of various users for comparison purposes and in order to make appropriate recommendations for reducing a carbon footprint, energy usage, etc., as seen in the following excerpts:
[0029] The users l 20A-N may register for a service for providing actions to reduce a carbon footprint provided by the service provider 110. The users l 20A-N may provide personal information to the service provider 110, such as name, address, social security number, or generally any information that may be used to obtain energy use data from the utility providers 140. The users 120A-N may manually enter energy use information used to determine a carbon footprint of the users 120A-N. The energy use information may be specific to the user. For example, the users 120A-N may enter information about the square footage of their home, the number of bedrooms, the vehicles they own, the vehicle usage, the types of appliances in the home, the sources of energy to the home, such as electricity, natural gas, propane, coal, solar panels, and any other information. Depending on an implementation, energy use information may also be gathered in other ways, such as directly via the utility providers 140. For example, the utility network control systems 330 may provide an application programming interface (API) to allow the service provider 110 to receive energy use information from the utility providers 140. The service provider 110 may use the information provided by the users 120A-N and/or other sources, for example the transaction data providers 140 and reference data providers 145, to determine energy use and a carbon footprint of the users 120A-N.
[0032] Since the use of a particular type of energy may vary depending upon the geographic location of the users 120A-N, the energy use of the user A 120A may be compared against the energy use of users 120B-N located within the same geographic area as the user A 120A. Alternatively or in addition, the users 120BN may be located in a geographic area with the same climate as the user A 120A, or within an area where the electric energy providers use the same fuel mix to provide electricity, such as a mix of renewable energy, nuclear energy, natural gas, coal, and/or generally any type of energy capable of producing electricity. The carbon footprint data may also include a trend analysis. The trend analysis may display the historical emissions data of the users 120A-N and may indicate whether the users 120A-N are increasing or decreasing their emissions.
[0050] At block 440 the service provider 110 may determine carbon reducing actions relating to the energy use/emission data of the user A 120A. The actions may be tailored to each user such as based on actual energy uses of the user. The
actions may be prioritized in accordance with a relative impact that the action has on a carbon footprint of the user. Actions that provide a greater impact on the carbon footprint may be emphasized. Alternatively and/or additionally, the service provider 110 may compare the emissions data of the user A 120A to the emissions data of the users 120B-N. The set of users 120B-N may be the users 1208-N located in the same geographic region as the user A 120A, the users 1208-N located in a similar climate as the user A 120A, the users 120B-N with similar size/age homes as the user A 120A, or generally any segment of the users 120B-N which may indicate the relative emissions of the user A 120A. The service provider 110 may determine relative emissions of the user A 120A for each energy use type. The service provider 110 may provide recommended actions to the user A 120A geared towards the energy use types where the user A 120A is generating relatively large amounts of carbon emissions.
[0053] At block 455 the service provider 110 may display the emission data and the recommended actions to the user A 120A. The emission data may be displayed in a bar graph, a pie chart, or generally any method of displaying the data so as to convey the carbon emissions of the user A 120A. The emissions data of the user A 120A may be compared against users 1208-N with similar energy needs of the user A 120A in order to provide a relative perspective of the energy consumption of the user A 120A.
[0057] At block 520 the service provider 110 may determine whether the user A 120A should be recommended actions to reduce their emissions due to electricity usage. If the emissions due to the electricity use of the user A 120A are higher than the emissions due to the electricity use of similar users 120B-N, the system may move to block 525. If the emissions due to the electricity use of the user A 120A are less than or equal to emissions due to the electricity use of similar users 120B-N, the system 200 may move to block 530.
(0058] At block 525 the service provider 110 may determine a carbon reducing action related to electricity use. For example, the service provider 110 may recommend that the user A 120A replace energy consuming appliances, such as a washer/dryer, with more efficient appliances. Alternatively or in addition, the service provider 110 may recommend the user A 120A unplug unneeded appliances, such as a third refrigerator. (Emphasis added)
The home of each of Volkman’s users is analogous to each of the buildings in Abramson. Volkman looks are relative usage to identify areas in greater need of being addressed to reduce carbon emissions, including those related to electricity use (which implies that higher electricity use is associated with a larger carbon footprint and greater carbon emissions). When a user uses more energy than comparable users, as gleaned from utility data for each of multiple users, then a recommendation is made for that user to reduce carbon emissions, electricity use, etc. In effect, the claim limitation “scoring the potential sustainable infrastructure projects comprises improving a first score for the first project relative to a second score for the second project if the first utility rate is higher than the second utility rate” adds a consideration of greater criticality when one utility rate (such as energy usage) is relatively higher than another utility rate. The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Abramson to incorporate the following:
the utility information comprises a first grid emissions rate associated with energy generated for provision to the first building and a second grid emissions rate associated with energy generated for provision to the second building; and
scoring the sustainable infrastructure projects comprises improving a first score for the first project relative to a second score for the second project if the first grid emissions rate is higher than the second grid emissions rate
in order to allow for areas of greater criticality (such as above average carbon footprints, energy usage, etc.) to be emphasized when ranking projects by greatest impact, urgency, etc., thereby allowing for projects deemed to be of greater importance to be given higher priority relative to less critical projects.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gitt et al. (US 2016/0321587) – Discusses utilities and household equipment upgrades.
Smith et al. (US 6,785,592) – Manages energy.
Sloop et al. (US 2015/0192911) – Optimizes energy consumption in a building.
Amminudin et al. (US 2014/0026085) – Evaluates top performance priorities for a gas oil separation plant.
Sussmeier et al. (US 2010/0153176) – Prioritizes recommendations that will give the most drastic carbon emission improvement for the least amount of cost and effort (¶ 40).
Sadwick (US 2019/0021154) – Expresses energy cost in kWH times the rate in $/kWH (¶ 144).
Sutton et al. (US 2013/0007954) – Expresses an energy cost based on a monetary rate charged per kWh.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUSANNA M DIAZ whose telephone number is (571)272-6733. The examiner can normally be reached M-F, 8 am-4:30 pm.
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A
Prosecution Insights