DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to communications on 04/14/2025
Claims 1-20 are pending
Claims 1-20 are rejected
Priority
Application Data Sheet received on 04/14/2025 claims domestic priority to provisional application 63/426092 with filling date 11/17/2022. Application Data sheet accepted by the examiner.
Drawings
Drawings received on 03/29/2023 is accepted by the examiner.
Specification
Abstract received on 03/29/2023 is less than 150 words and contains no legal or implied phraseology. Abstract is accepted by the examiner.
Specifications received on 03/29/2023 has been reviewed and accepted by the examiner.
Claim Interpretation
Facility/building: Par 38: “include any number of facilities 204 (also referred to interchangeably herein as "buildings," "physical premises," or the like),” … par 40: “The facilities 204 may include any commercial, residential, public, recreational, or other physical infrastructure or the like that uses, or that may consider using, renewable energy to lower costs, manage carbon footprint, satisfy ESG (Environmental, Social, Governance) demands or similar, or otherwise meet corporate objectives or the like.”
As informed by paragraphs 38 and 40, the terms “facility” and “building” are interpreted as being interchangeable in the claims. With the broadest reasonable interpretation of both terms encompassing the broadest reasonable interpretation of a building.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “result effective variables” in claims 1, 6, 16, and 17 is a relative term which renders the claim indefinite. It is unclear what the term “result effective variables” means as opposed to “variables”
Claim 8 is rejected for lacking antecedent basis for the term “the carbon target.”
Claim 14 is rejected for being indefinite: The claim states “wherein taking an action includes transmitting a request for a responsive action from a user to a difference between the carbon offset estimate and a carbon target for the facility during the interval.” As written, the scope of the claim is uncertain. Based on the specifications, this claim can be understood in two separate ways. “wherein taking an action includes transmitting a request for a responsive action from a user to reduce a difference between the carbon offset estimate and a carbon target for the facility during the interval” or “wherein taking an action includes transmitting a request for a responsive action from a user in response to a difference between the carbon offset estimate and a carbon target for the facility during the interval.”
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention recites a judicial exception, an abstract idea, which has not been integrated into practical application and the claims further do not recite significantly more than the judicial exception.
Independent Claims:
Claim 1
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites A predictive analytics system for resource management at a facility with at least one colocated renewable energy source which is a machine.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 1 recites: executing a classification engine to identify a building type, the classification engine applying a k-nearest neighbor model configured to categorize a building type based on at least a building size, a building usage, and a location demographic profile;
Identifying a building type is the process of taking in input data (building size, usage, and location demographic) and using it to determine its type (residential, commercial etc.). This process can be performed by an individual reasonably skilled in the art through an observation of the data and an evaluation of the data. (this building is small, has low energy usage, and has a small demographic of people, this building is determined to be a residential house). MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Therefore the above claim limitation recites a mental process.
The application of a k-nearest neighbor model is the application of a mathematic model which uses distance metric for a clustering algorithm. This is a mathematic algorithm which clusters data based on parameters (size, usage and location demographic). The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Therefore the above claim limitation also recites the abstract idea of a mathematic calculation.
executing a first predictive engine to predict renewable energy generation from the at least one colocated renewable energy source based on meteorological data, the first predictive engine using a predictive model trained to apply result effective variables to estimate an output for a renewable resource type and a physics model to adjust the output according to one or more objective features of the renewable resource type;
The predictive model as described above is a model which uses variables to provide an output (renewable energy generation) based on an input (meteorological data). This claim includes a regression model which is trained to predict these numeric values. The physics model is a model which scales the output of the predictive model by mathematic values according to “objective features” of the renewable resource. For example, a house may have multiple solar panels, the predictive model is trained to describe the behavior of solar panels based on forecasted weather conditions in the area across a period of time. The physics model is then used to scale the data according to the actual physical solar panel which is installed on the home. As outlined, this claim limitation pertains to the processing of data to generate outputs using mathematic models. The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Therefore this claim recites an abstract idea.
executing a second predictive engine to predict carbon production from the facility based on the building type, the second predictive engine using a supervised machine learning model trained using historical carbon production data for the building type and temporally corresponding historical meteorological conditions;
The second predictive engine includes regression type mathematic modeling which generates an output (predicted carbon production) based on inputs (building type and meteorological conditions). As explained above in relation to the first predictive engine, this claim limitation pertains to the processing of data to generate outputs using mathematic models. The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Therefore this claim recites an abstract idea.
determining the building type for the facility with the classification engine;
As stated above Identifying a building type is the process of taking in input data (building size, usage, and location demographic) and using it to determine its type (residential, commercial etc.). This process can be performed by an individual reasonably skilled in the art through an observation of the data and an evaluation of the data. (this building is small, has low energy usage, and has a small demographic of people, this building is determined to be a residential house). MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Therefore the above claim limitation recites a mental process.
simulating a renewable energy output for the facility over the interval with the first predictive engine based on the meteorological prediction data for the interval;
As stated above, simulating a renewable energy output for the facility with the first predictive engine is the use of a mathematic model to calculate a numeric renewable energy amount using a given equation. The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Therefore the claim recites an abstract idea.
simulating a carbon output for the facility over the interval with the second predictive engine based on the building type from the classification engine and the meteorological prediction data for the interval;
As stated above, simulating carbon output for the facility with the second predictive engine is the use of a mathematic model to calculate a numeric carbon amount using a given equation. The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Therefore the claim recites an abstract idea.
calculating the carbon offset estimate based on a difference between the renewable energy output and the carbon output over the interval;
This claim defines the carbon offset estimate as a difference between two numerical outputs determined earlier in the claim. The MPEP 2106.04(a)(2)(I)(A) states “A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A), or it can be set forth in the form of an equation such as p = F/A” Therefore this claim recites a mathematical relationship and is an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 1 additionally recites , the system including computer executable code stored in a non-transitory computer readable medium that, when executing on one or more computing devices, causes the one or more computing devices to perform the steps of:
This claim limitation recites general purpose computing devices which perform the abstract ideas outlined above. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore this claim limitation does not integrate a judicial exception into a practical application or provide significantly more.
receiving a request for a carbon offset estimate for the facility over an interval;
This claim limitation states that the system receives a request to perform the abstract idea of generating a carbon offset estimate as outlined above. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, the receiving of a request does not integrate a judicial exception into a practical application or provide significantly more.
retrieving meteorological prediction data for the interval from one or more remotely hosted meteorological services;
This claim states that the system receives meteorological data from a meteorological service. A meteorological service is a service which supplied meteorological data. The claim does not limit or state how the data is received, just that it is received by the system. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Because this claim states that metrological prediction data is received by the system using a meteorological service, this limitation does not integrate a judicial exception into a practical application or provide significantly more.
and taking an action based on the carbon offset estimate.
The claim limitation does not define the action taken, does not tie the action back into the claim, nor describe the mechanism in which the action is taken. The MPEP 2106.05(f)(1) states “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words ‘apply it’” Therefore this limitation does not integrate a judicial exception into a practical application or provide significantly more.
facility … carbon …
Throughout the claim, the terms of the art used such as facility, carbon offset, etc indicate that the judicial exceptions described above are used and applied to measuring and analyzing data in relation to carbon energy in houses. MPEP 2106.05(h) states “limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” with an example being “vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment,” Therefore this claim limitations does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, the above claim limitations do not recite additional elements that amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 1 is not eligible under 35 USC 101.
Claim 6:
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites A method for predictive analysis of carbon budgets for a facility with at least one colocated renewable energy source, the method comprising: which is a process.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 6 recites: processing building data for the facility with a classification engine to determine the building type for the facility, where the classification engine applies a k-nearest neighbor model configured to categorize the building type based on the building data, and wherein the building data includes at least a building size, a building usage, and a location demographic profile;
Identifying a building type is the process of taking in input data (building size, usage, and location demographic) and using it to determine its type (residential, commercial etc.). This process can be performed by an individual reasonably skilled in the art through an observation of the data and an evaluation of the data. (this building is small, has low energy usage, and has a small demographic of people, this building is determined to be a residential house). MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Therefore the above claim limitation recites a mental process.
The application of a k-nearest neighbor model is the application of a mathematic model which uses distance metric for a clustering algorithm. This is a mathematic algorithm which clusters data based on parameters (size, usage and location demographic). The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Therefore the above claim limitation also recites the abstract idea of a mathematic calculation.
simulating a renewable energy generation from the at least one colocated renewable energy source with a first predictive engine, the first predictive engine calculating energy generation using (a) a predictive model trained to estimate an output for a renewable resource type based on one or more result effective variables and (b) a physics model to adjust an output of the predictive model according to one or more objective features of the renewable resource type;
The predictive model as described above is a model which uses variables to provide an output (renewable energy generation) based on an input (meteorological data). This claim includes a regression model which is trained to predict these numeric values. The physics model is a model which scales the output of the predictive model by mathematic values according to “objective features” of the renewable resource. For example, a house may have multiple solar panels, the predictive model is trained to describe the behavior of solar panels based on forecasted weather conditions in the area across a period of time. The physics model is then used to scale the data according to the actual physical solar panel which is installed on the home. As outlined, this claim limitation pertains to the processing of data to generate outputs using mathematic models. The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Therefore this claim recites an abstract idea.
simulating a carbon production from the facility with a second predictive engine, the second predictive engine calculating the carbon production using the meteorological prediction data for the interval and a supervised machine learning model trained using historical carbon production data for the building type and temporally corresponding historical meteorological conditions;
The second predictive engine includes regression type mathematic modeling which generates an output (predicted carbon production) based on inputs (building type and meteorological conditions). As explained above in relation to the first predictive engine, this claim limitation pertains to the processing of data to generate outputs using mathematic models. The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Therefore this claim recites an abstract idea.
calculating the carbon offset estimate based on a difference between the renewable energy output and the carbon output over the interval;
This claim defines the carbon offset estimate as a difference between two numerical outputs determined earlier in the claim. The MPEP 2106.04(a)(2)(I)(A) states “A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words or using mathematical symbols. For example, pressure (p) can be described as the ratio between the magnitude of the normal force (F) and area of the surface on contact (A), or it can be set forth in the form of an equation such as p = F/A” Therefore this claim recites a mathematical relationship and is an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 6 additionally recites receiving a request for a carbon offset estimate for the facility over an interval;
This claim limitation states that the system receives a request to perform the abstract idea of generating a carbon offset estimate as outlined above. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, the receiving of a request does not integrate a judicial exception into a practical application or provide significantly more.
retrieving meteorological prediction data for the interval from one or more remotely hosted meteorological services;
This claim states that the system receives meteorological data from a meteorological service. A meteorological service is a service which supplied meteorological data. The claim does not limit or state how the data is received, just that it is received by the system. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Because this claim states that metrological prediction data is received by the system using a meteorological service, this limitation does not integrate a judicial exception into a practical application or provide significantly more.
and taking an action based on the carbon offset estimate.
The claim limitation does not define the action taken, does not tie the action back into the claim, nor describe the mechanism in which the action is taken. The MPEP 2106.05(f)(1) states “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words ‘apply it’” Therefore this limitation does not integrate a judicial exception into a practical application or provide significantly more.
facility … carbon …
Throughout the claim, the terms of the art used such as facility, carbon offset, etc indicate that the judicial exceptions described above are used and applied to measuring and analyzing data in relation to carbon energy in houses. MPEP 2106.05(h) states “limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” with an example being “vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment,” Therefore this claim limitations does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, the above claim limitations do not recite additional elements that amount to significantly more than the judicial exception.
Based on the above facts, the office concludes that claim 6 is not eligible under 35 USC 101.
Claim 17
Step 1: Is the claimed invention one of the four statutory categories? :
YES. The claim recites A system for predictive analysis of carbon budget data based on third-party meteorological services and a local facility classification engine, the system comprising: which is a machine.
Step 2A Prong 1, inquiry "Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?":
YES. Claim 17 recites:
a classification engine configured to categorize a building type using a k- nearest neighbor model based on objective building parameters,
Identifying a building type is the process of taking in input data (building size, usage, and location demographic) and using it to determine its type (residential, commercial etc.). This process can be performed by an individual reasonably skilled in the art through an observation of the data and an evaluation of the data. (this building is small, has low energy usage, and has a small demographic of people, this building is determined to be a residential house). MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Therefore the above claim limitation recites a mental process.
The application of a k-nearest neighbor model is the application of a mathematic model which uses distance metric for a clustering algorithm. This is a mathematic algorithm which clusters data based on parameters (size, usage and location demographic). The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Therefore the above claim limitation also recites the abstract idea of a mathematic calculation.
a first predictive engine configured to simulate renewable energy generation from the renewable energy source using a predictive model trained to apply result effective variables to estimate an output for a renewable resource type and a physics model to adjust the output according to one or more objective features of the renewable resource type,
The predictive model as described above is a model which uses variables to provide an output (renewable energy generation) based on an input (meteorological data). This claim includes a regression model which is trained to predict these numeric values. The physics model is a model which scales the output of the predictive model by mathematic values according to “objective features” of the renewable resource. For example, a house may have multiple solar panels, the predictive model is trained to describe the behavior of solar panels based on forecasted weather conditions in the area across a period of time. The physics model is then used to scale the data according to the actual physical solar panel which is installed on the home. As outlined, this claim limitation pertains to the processing of data to generate outputs using mathematic models. The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Therefore this claim recites an abstract idea.
and a second predictive engine configured to simulate a carbon production from the physical site based on at least the meteorological prediction data and the building type, the second predictive engine using a supervised machine learning model trained using historical carbon production data for the building type and temporally corresponding historical meteorological conditions,
The second predictive engine includes regression type mathematic modeling which generates an output (predicted carbon production) based on inputs (building type and meteorological conditions). As explained above in relation to the first predictive engine, this claim limitation pertains to the processing of data to generate outputs using mathematic models. The MPEP 2106.04(a)(2)(I)(C) states “A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. A mathematical calculation is a mathematical operation (such as multiplication) or an act of calculating using mathematical methods to determine a variable or number, e.g., performing an arithmetic operation such as exponentiation. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.” Therefore this claim recites an abstract idea.
determine, with the classification engine, the building type associated with the physical site,
As stated above Identifying a building type is the process of taking in input data (building size, usage, and location demographic) and using it to determine its type (residential, commercial etc.). This process can be performed by an individual reasonably skilled in the art through an observation of the data and an evaluation of the data. (this building is small, has low energy usage, and has a small demographic of people, this building is determined to be a residential house). MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Therefore the above claim limitation recites a mental process.
calculate, with the first predictive engine, an expected output during the interval from the renewable energy source colocated with the physical site,
As stated above, simulating a renewable energy output for the facility with the first predictive engine is the use of a mathematic model to calculate a numeric renewable energy amount using a given equation. The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Therefore the claim recites an abstract idea.
calculate, with the second predictive engine, an expected carbon production during the interval from the physical site, and
As stated above, simulating carbon output for the facility with the second predictive engine is the use of a mathematic model to calculate a numeric carbon amount using a given equation. The MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” Therefore the claim recites an abstract idea.
Step 2A Prong 2, Does the claim recite additional elements that integrate the judicial exception into a practical application?
NO. Claim 17 additionally recites a physical site including a building, a renewable energy source, and a plurality of sensors for monitoring energy usage at the building; a database storing data acquired from the plurality of sensors; and a server hosting renewable energy management resources for the physical site,
This claim limitation recites general purpose computing devices which perform the abstract ideas outlined above. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore this claim limitation does not integrate a judicial exception into a practical application or provide significantly more.
Furthermore Throughout the claim limitation, the terms of the art used such as facility, carbon offset, etc., as well as the presence of a physical site, energy source, etc. indicate that the judicial exceptions described above are used and applied to measuring and analyzing data in relation to carbon energy in houses. MPEP 2106.05(h) states “limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” with an example being “vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment,” Therefore this claim limitations does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application
the renewable energy management resources including: a programmatic interface to a remote service configured to provide meteorological prediction data,
This claim provides meteorological data from a meteorological service. A meteorological service is a service which supplied meteorological data. The claim does not limit or state how the data is received, just that it is received by the system. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Because this claim states that metrological prediction data is received by the system using a meteorological service, this limitation does not integrate a judicial exception into a practical application or provide significantly more.
and the server configured to receive, in a user interface rendered by the server, an input of an interval for processing carbon data for the physical site,
this limitation has a user input the interval that they would like to be tested/simulated. The Mere discusses Data Gathering: 2106.05(g) with an example being “o i. Performing clinical tests on individuals to obtain input for an equation, In re Grams, 888 F.2d 835, 839-40; 12 USPQ2d 1824, 1827-28 (Fed. Cir. 1989);” This limitation is requesting information from individuals to obtain input for an equation. And therefore the limitation is mere data gathering.
retrieve, through the programmatic interface to the remote service, the meteorological prediction data for the interval identified in the input,
This claim states that the system receives meteorological data from a meteorological service. A meteorological service is a service which supplied meteorological data. The claim does not limit or state how the data is received, just that it is received by the system. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Because this claim states that metrological prediction data is received by the system using a meteorological service, this limitation does not integrate a judicial exception into a practical application or provide significantly more.
initiate, in response to a disparity between the expected output of the renewable source and the expected carbon production of the physical site, a responsive action for the physical site.
The claim limitation does not define the action taken, does not tie the action back into the claim, nor describe the mechanism in which the action is taken. The MPEP 2106.05(f)(1) states “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words ‘apply it’” Therefore this limitation does not integrate a judicial exception into a practical application or provide significantly more.
Step 2B, does the claim recites additional elements that amount to significantly more than the judicial exception.
NO. As stated in Step 2A Prong 2, and the server configured to receive, in a user interface rendered by the server, an input of an interval for processing carbon data for the physical site,
This limitation was determined to be mere data gathering. Furthermore this limitation is well understood. It is well understood and routine to request information from a user and recording that information. This is tangential to “i. Recording a customer’s order, Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016);” which is consider to be well understood and conventional in MPEP 2106.05(d)(ii) . Where this limitation records a user’s input to be later used in the equations above.
Based on the above facts, the office concludes that claim 17 is not eligible under 35 USC 101.
Dependent Claims:
Claim 2:The predictive analytics system of claim 1, further comprising code that performs the step of
comparing the carbon offset estimate for the facility to a carbon target for the facility,
This claim limitations compares the carbon offset estimate for the facility ( a numeric value determined above) to a carbon target for the facility (a target numeric value). This is a comparison of two numeric values based on an observation of the values and an evaluation of their differences (ie: one value is larger than the other). The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Therefore this claim is a further recitation of an abstract idea.
and generating a user recommendation based on a difference between the carbon offset estimate and the carbon target.
This claim limitation states to recommend an action based on the difference between the two values determined above. The claim does not limit what the recommendations are, the steps to accomplish them, nor does it tie the recommendation back to the claim (ie: if the difference is >5% recommend A, if >50% recommend B). The MPEP 2106.05(f)(1) states “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words ‘apply it’” Therefore this claim limitation does not integrate a judicial exception into a practical application or provide significantly more.
Claim 3:
The predictive analytics system of claim 1, further comprising code that performs the steps of
acquiring data from one or more sensors at the facility to monitor a current energy generation from the at least one colocated renewable energy source,
This claim limitations states that sensors are used in the facility to monitor the energy generated by the renewable energy source. The MPEP gives examples of Mere Data Gathering: 2106.05(g) to be “vi. Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis).” This claim limitation pertains to measuring data derived from a sensor to be used in an analysis, therefore this claim is directed to mere data gathering.
The examiner outlines why this mere data gathering is also considered well understood routine and conventional. This claim limitation pertains to the usage of a sensor at a facility to measure energy generation from the energy source present at the facility. As understood by one ordinarily skilled in the art, the usage of a sensor to measure energy generation is the usage of a tool (a sensor) for its ordinary purpose (measuring energy generated in the facility). Therefore, this limitation which pertains to mere data gathering is also considered to be well understood and conventional.
and updating the carbon offset estimate for the interval based on the current energy generation.
This claim limitation updates a numeric value (the carbon offset estimate) based on another numeric value (current energy generation). This is a judgement call which can be performed by an individual (if the current energy generation is >50% of the carbon estimate, increase the estimate). MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Therefore the claim limitation recites an abstract idea.
Claim 4:
The predictive analytics system of claim 1, further comprising code that performs the steps of
acquiring data from one or more sensors at the facility to monitor a current electrical consumption at the facility,
This claim limitations states that sensors are used in the facility to monitor the electrical consumption used by the facility. The MPEP gives examples of Mere Data Gathering: 2106.05(g) to be “vi. Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis).” This claim limitation pertains to measuring data derived from a sensor to be used in an analysis, therefore this claim is directed to mere data gathering.
The examiner outlines why this mere data gathering is also considered well understood routine and conventional. This claim limitation pertains to the usage of a sensor at a facility to measure electricity used by the facility. As understood by one ordinarily skilled in the art, the usage of a sensor to measure energy usage is the usage of a tool (a sensor) for its ordinary purpose (measuring electricity usage in the facility). Therefore, this limitation which pertains to mere data gathering is also considered to be well understood and conventional.
and updating the offset estimate for the interval based on the current electrical consumption.
This claim limitation updates a numeric value (the carbon offset estimate) based on another numeric value (current electrical consumption). This is a judgement call which can be performed by an individual (if the electrical consumption is >50% of the carbon offset, increase the offset). MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Therefore the claim limitation recites an abstract idea.
Claim 5:The predictive analytics system of claim 1, wherein the colocated renewable energy source includes at least one of a solar power source or a wind turbine.
This claim states that the renewable energy source is either solar power or wind turbine. This claim limitation states that the judicial exceptions outlined above are applied to solar power or wind turbines. MPEP 2106.05(h) states “limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” Therefore this claim limitation does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Claim 7:
The method of claim 6, further comprising comparing the carbon offset estimate for the facility to a carbon target for the facility.
This claim limitations compares the carbon offset estimate for the facility ( a numeric value determined above) to a carbon target for the facility (a target numeric value). This is a comparison of two numeric values based on an observation of the values and an evaluation of their differences (ie: one value is larger than the other). The MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Therefore this claim is a further recitation of an abstract idea.
Claim 8:
The method of claim 6, further comprising displaying a comparison of the carbon offset estimate to the carbon target for the facility in a user interface.
This claim limitation displays the comparison of the carbon offset estimate to the carbon target in a user interface. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, the usage of the computing device to perform a general purpose computer activity (displaying data on a screen) does not integrate a judicial exception into a practical application or provide significantly more.
Claim 9:
The method of claim 6, further comprising:
instrumenting the facility with a first one or more sensors to monitor a current energy generation from the at least one colocated renewable energy source at the facility;
This claim limitations states that sensors are used in the facility to monitor the energy generated by the renewable energy source. The MPEP gives examples of Mere Data Gathering: 2106.05(g) to be “vi. Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis).” This claim limitation pertains to measuring data derived from a sensor to be used in an analysis, therefore this claim is directed to mere data gathering.
The examiner outlines why this mere data gathering is also considered well understood routine and conventional. This claim limitation pertains to the usage of a sensor at a facility to measure energy generation from the energy source present at the facility. As understood by one ordinarily skilled in the art, the usage of a sensor to measure energy generation is the usage of a tool (a sensor) for its ordinary purpose (measuring energy generated in the facility). Therefore, this limitation which pertains to mere data gathering is also considered to be well understood and conventional.
and instrumenting the facility with a second one or more sensors to monitor a current electrical consumption at the facility.
This claim limitations states that sensors are used in the facility to monitor the electrical consumption used by the facility. The MPEP gives examples of Mere Data Gathering: 2106.05(g) to be “vi. Determining the level of a biomarker in blood, Mayo, 566 U.S. at 79, 101 USPQ2d at 1968. See also PerkinElmer, Inc. v. Intema Ltd., 496 Fed. App'x 65, 73, 105 USPQ2d 1960, 1966 (Fed. Cir. 2012) (assessing or measuring data derived from an ultrasound scan, to be used in a diagnosis).” This claim limitation pertains to measuring data derived from a sensor to be used in an analysis, therefore this claim is directed to mere data gathering.
The examiner outlines why this mere data gathering is also considered well understood routine and conventional. This claim limitation pertains to the usage of a sensor at a facility to measure electricity used by the facility. As understood by one ordinarily skilled in the art, the usage of a sensor to measure energy usage is the usage of a tool (a sensor) for its ordinary purpose (measuring electricity usage in the facility). Therefore, this limitation which pertains to mere data gathering is also considered to be well understood and conventional.
Claim 10:
The method of claim 9, further comprising
updating the carbon offset estimate for the interval based on data from the first one or more sensors and the second one or more sensors during the interval.
This claim limitation updates a numeric value (the carbon offset estimate) based on another numeric value (current electrical consumption or renewable energy production). This is a judgement call which can be performed by an individual (if the electrical consumption is >50% of the carbon offset, increase the offset). MPEP 2106.04(a)(2)(III) states “Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. “ Therefore the claim limitation recites an abstract idea.
Claim 11:
The method of claim 10, further comprising presenting the updated carbon offset estimate to a user.
This claim limitation displays the updated carbon offset estimate to a user interface. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, the usage of the computing device to perform a general purpose computer activity (displaying data on a screen) does not integrate a judicial exception into a practical application or provide significantly more.
Claim 12:The method of claim 9, further comprising normalizing and aggregating sensor data from the second one or more sensors for use in at least one of peer benchmarking and machine learning.
Sensor data is numerical data. Normalization and aggregating are forms of mathematic data processing. Peer benchmarking and machine learning use the numeric data to form comparisons or derive mathematic equations. MPEP 2106.04(a)(2)(III)(B) states “If a claim recites a limitation that can practically be performed in the human mind, with or without the use of a physical aid such as pen and paper, the limitation falls within the mental processes grouping, and the claim recites an abstract idea.” These claims pertain to the analysis/processing of data which recites a mental process.
Claim 13:The method of claim 9, wherein the second one or more sensors include one or more device monitors within the facility.
This claim limitation refers to the sensors which receive data which are used in the abstract ideas above. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, the usage of the computing device (a device monitor) to perform a general purpose computer activity (displaying sensor data on a screen) does not integrate a judicial exception into a practical application or provide significantly more.
Claim 14: The method of claim 6, wherein taking an action includes transmitting a request for a responsive action from a user to a difference between the carbon offset estimate and a carbon target for the facility during the interval.
This claim limitation transmits a request to a user. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, the usage of computer machinery to send a request for an action to a user, does not does not integrate a judicial exception into a practical application or provide significantly more, especially when the claim itself does not tie the action of the user back into the claim, nor limit what actions the user may perform.
Claim 15:The method of claim 6,
wherein taking an action includes generating an automated action to reduce the carbon offset estimate.
This claim limitation pertains to the “taking an action” in claim 6. This claim limitation still does not define the action taken, does not tie the action back into the claim, nor describe the mechanism in which the action is taken. The MPEP 2106.05(f)(1) states “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words ‘apply it’” Therefore this limitation does not integrate a judicial exception into a practical application or provide significantly more.
Claim 16:
The method of claim 6,
wherein the one or more result effective variables for the predictive model include at least one variable from the meteorological prediction data
This claim limitations states that the mathematic variables used in the abstract idea above are from meteorological prediction data. The MPEP 2106.05(h) states “limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” with an example being “vi. Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment,” Therefore this claim limitations does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application
Claim 18:The system of claim 17, wherein the responsive action includes an automatic action by the server to reduce the disparity.
This claim limitation pertains to the “taking an action” in claim 17. This claim limitation still does not define the action taken, does not tie the action back into the claim, nor describe the mechanism in which the action is taken. The MPEP 2106.05(f)(1) states “The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words ‘apply it’” Therefore this limitation does not integrate a judicial exception into a practical application or provide significantly more.
Claim 19:The system of claim 17, wherein the responsive action includes transmitting a notification concerning the disparity to an administrator.
This claim limitation transmits information to an administrator. The MPEP 2106.05(f)(2) states “Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, the usage of computer machinery to send a notification to an administrator does not does not integrate a judicial exception into a practical application or provide significantly more, especially when the claim itself does not tie the action of the user back into the claim, nor limit what actions the user may perform.
Claim 20:
This claim states that the renewable energy source is either solar power or wind turbine. This claim limitation states that the judicial exceptions outlined above are applied to solar power or wind turbines. MPEP 2106.05(h) states “limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application” Therefore this claim limitation does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tennant_2020 (US 20230139514 A1) Jayan_2021 (US 20220344934 A1) Lingras_2020 (US 20200151836 A1) Vogt_2022 (“Introducing a comprehensive physics-based modelling framework for tandem and other PV systems”) And Tuank_2019 (“MACHINE LEARNING CLASSIFICATION WITH K-NEAREST NEIGHBOURS”)
Claim 1:Tennant_2020 makes obvious A predictive analytics system for resource management at a abstract: “ A method for managing an energy system having one or more renewable energy sources, one or more energy storage devices, one or more loads, and a grid connection for connecting at least temporarily to an external energy distribution grid is disclosed. The method generates a prediction of energy demand of the loads using historical energy demand data, and a prediction of renewable energy availability from the renewable energy sources using weather forecast data” .. par 6: “ Even for grid-connected systems (e.g. residential buildings there is often a desire to reduce reliance on grid-supplied power by making use of renewable sources such as solar panel installations, but existing systems do not always allow effective management of the available energy sources.) Par 104: “ Referring to FIG. 1b, a partially isolated energy system 100b will now be described. The energy system 100b is preferably that of a residential environment having a limited grid connection. This may include housing where power shortages are common, or where consumption of renewable energy is preferred to and/or cheaper than that of grid-derived energy.”) the system including computer executable code stored in a non-transitory computer readable medium that, when executing on one or more computing devices, causes the one or more computing devices to perform the steps of: (par 45: “ Embodiments also provide a system having means, optionally in the form of one or more processor(s) with associated memory, for performing any method as set out herein and a non-transitory computer-readable medium comprising software code adapted, when executed by a data processing system, to perform any method as set out herein.”)
executing a first predictive engine to predict renewable energy generation from the at least one colocated renewable energy source based on meteorological data, (par 109: “ the generation 220 of the renewable energy availability prediction 225 is performed by a model based on at least the weather forecast data 206. The weather forecast data is combined with a model predicting an expected amount of available renewable energy in various different weather conditions. In a preferred embodiment, two alternative models are implemented. The first, referred to as the statistical model, uses past observed data concerning performance of the specific system in various conditions. The second uses a predetermined formula which may be adapted to the specific system by adjusting parameters of the formula. The following examples are described for a simple system with a single renewable source in the form of a solar cell installation but can be extended to more complex systems with multiple energy sources.”) the first predictive engine using a predictive model trained to apply result effective variables to estimate an output for a renewable resource type (par 117: “ The formulaic method provides the weather forecast data 206 as input to a formula, the formula parameterised using weather parameters, to produce the prediction 225 of renewable energy availability over the duration of the weather forecast data 206, for example 24 hours, as a continuous or quasi-continuous signal. This can give smoother predicted energy profiles over the course of a day, which can require less extreme system responses. The weather parameters can represent relationships between the renewable energy availability and various states, such as the season, whether it is raining, cloudy, sunny, windy or snowing, or a property of the system 100 such as an orientation angle of a solar panel and other parameters determining the efficiency of the solar panel or other renewable source”) and a the output according to one or more objective features of the renewable resource type; (par 117: “ The user may adjust the formula parameters e.g. based on the known characteristics of their specific installation. Each parameter may be a value, such as a factor between said weather state and the renewable energy availability, or may be a classification of the relationship, such as strong, weak, none or inverse. Buffer parameters, such as a wind buffer or a solar buffer, may also be used to parameterise the formula by providing degrees of uncertainty regarding the corresponding weather parameter values and their effect on the predictions.”)
executing a second predictive engine to predict par 107: “ Referring to FIG. 2a, a method 200 for managing an energy system 100 will now be described. The energy system 100 may correspond to either of the previous examples 100a, 100b. Historical energy demand data 202 and measured component performances 204 are provided to the step 210 of generating a prediction 215 of energy demand of the system 100. “) the second predictive engine using par 107: “ Referring to FIG. 2a, a method 200 for managing an energy system 100 will now be described. The energy system 100 may correspond to either of the previous examples 100a, 100b. Historical energy demand data 202 and measured component performances 204 are provided to the step 210 of generating a prediction 215 of energy demand of the system 100. In generating 210 the prediction 215, the measured component performances 204 are analysed by comparing them with corresponding rated performances 205, providing feedback to the prediction process allowing it to learn. The measured component performances 204 may be expressed as one or more performance metrics and may include the energy generated by each renewable energy source 110, component temperatures, and the energy capacity (measured in Amp-hours) of the energy storage devices 130. Other types of analyses may be made, such as monitoring long-term trends of the system performance and how it may correlate to external conditions such as weather.”) par 110: “ Data points within the observed performance data are each assigned to one of the buckets based on the bucket parameters (time, weather conditions etc), which may be obtained from a weather service or may be locally measured.” … par 116: “ The step 210 of generating the prediction of energy demand 215 may be performed in a similar way, wherein timeframes within a desired duration of the prediction 215 are assigned to buckets, and the prediction in each bucket is determined based on one or more observed data points within that bucket, for example using the mean energy demand of the observed data points within that bucket. The predictions for each bucket are then combined to produce the prediction 215 of energy demand.”) ) and temporally corresponding historical meteorological conditions; (par 36: “ The demand parameters may comprise one or more of the weather parameters. This can allow any dependency on weather of power usage to be accounted for in the prediction of energy demand. For example, if the temperature is very cold or very hot, the energy demand from heating or air conditioning systems may be likely to increase.”)
receiving a request for a par 122: “ The amount of grid-derived energy required may be determined by predicting a state of charge of the energy storage devices 130 over a predetermined time period using the predictions 215, 225”)
retrieving meteorological prediction data ( par 154: “ A network interface 320 communicates with a server 340 via the internet in order to receive data such as weather forecast data”) for the interval from one or more remotely hosted meteorological services; ( par 122: “ For example, if the weather forecast indicates good weather with plenty of sunshine and the predictions 215, 225 indicate low demand and high solar panel output respectively, then it can be determined that a lower amount of grid power will be required to supplement solar power in charging the batteries 130. “) par 18: “ The method may comprise identifying a bucket matching the determined prediction parameters, preferably one or more time and/or weather parameters corresponding to the given time and weather forecast data for the given time, “
simulating a renewable energy output for the facility over the interval with the first predictive engine based on the meteorological prediction data for the interval; (par 117: “ The formulaic method provides the weather forecast data 206 as input to a formula, the formula parameterised using weather parameters, to produce the prediction 225 of renewable energy availability over the duration of the weather forecast data 206”)
simulating a par 36: “ The demand parameters may comprise one or more of the weather parameters. This can allow any dependency on weather of power usage to be accounted for in the prediction of energy demand. For example, if the temperature is very cold or very hot, the energy demand from heating or air conditioning systems may be likely to increase.” Par 42: “ the method comprising the steps of: generating a prediction of energy demand of the loads using historical energy demand data; “)
calculating the par 121: “ In one example, the system determines the total predicted demand 210 for a given time period, and subtracts the predicted renewable energy availability 220 for that time period from the total demand to determine the amount of energy that needs to be obtained from the grid 120 in order to meet the predicted demand.”)
and taking an action par 148: “ The demand prediction 215, the renewable energy availability prediction 225, the real-time statuses 247, the weather forecast data 206 and historical temperature data 208 of the energy storage devices 130 are provided to the step 280 of generating a temperature prediction 282 for each energy storage device 130.” … par 149: “ The temperature prediction 282 and a corresponding real-time temperature 247 are provided to the step 285 of regulating the energy storage device temperature. The temperature is regulated 285 by determining amendments to the energy conservation strategy 232 which will constrain the use of the batteries 130 so that they do not overheat.”)) Examiner note: Where this is an action taken in respect to the demand and renewable energy created. But not necessarily based on the offset of those.
Tennant_2020 does not expressly recite facility
executing a classification engine to identify a building type, the classification engine applying a k-nearest neighbor model configured to categorize a building type based on at least a building size, a building usage, and a location demographic profile;
physics model
carbon production/output
building type
a supervised machine learning
determining the building type for the facility with the classification engine;
Jayan_2021 however makes obvious A predictive analytics system for resource management at a facility with at least one colocated renewable energy source (abstract: “ Aspects of the present disclosure provide systems, methods, and computer-readable storage media that leverage artificial intelligence and machine learning (ML) to forecast energy demand and to generate an energy plan for one or more facilities of an organization”)
par 70: “ In some implementations, the energy demand or generation may be forecasted based on building type or industry associated with the customers, local weather, building parameters (e.g., number of floors, area, typical occupancy, etc.).”
carbon production/output (par 3: “ One particular area in which organizations are focusing in their attempts to reduce carbon emissions is in controlling their use of non-renewable energy resources, such as coal, oil, natural gas, and the like.”)
building type (par 70: “ In some implementations, the energy demand or generation may be forecasted based on building type or industry associated with the customers, local weather, building parameters (e.g., number of floors, area, typical occupancy, etc.).”
a supervised machine learning (par 5: “ For example, a system described herein may be configured to forecast an occupancy at a facility of an organization and, based on the forecasted occupancy and other information such as historical energy demand, to forecast an energy demand for the facility that is more accurate than other energy forecasting systems. The system may implement trained machine learning (ML) models (e.g., neural networks, support vector machines, decision trees, random forests, or the like) (Examiner note: these are supervised learning techniques) to forecast the energy demand,”)
and taking an action based on the carbon offset estimate par 51: “To illustrate recommending actions to offset the environmental impact corresponding to the selected energy resources 116, the action recommendation engine 128 may select, from multiple preset actions, one or more actions to be recommended to offset the environmental impact corresponding to the selected energy resources 116, such as planting a particular quantity of trees and purchasing a particular number of RECs based on selecting a first percentage of non-renewable/non-sustainable energy resources and a second percentage of renewable/sustainable energy resources to be included in the selected energy resources 116. Each of the available actions may be associated with a respective positive environmental impact, a respective cost, and the like, and such information may be used by the action recommendation engine 128 in addition to the selected energy resources 116 to select the recommended actions 184. The action recommendation engine 128 may select the recommended actions 184 to satisfy a particular criterion (e.g., environmental impact threshold) or to prioritize or optimize a particular criterion (e.g., total cost, environmental impact, etc.). For example, the action recommendation engine 128 may determine a net environmental impact estimate based on the respective environmental impacts that correspond to each of the selected energy resources 116, and the action recommendation engine 128 may compare the net environmental impact estimate to one or more thresholds. Depending on the relationship between the net environmental impact estimate and the one or more thresholds, the action recommendation engine 128 may select one or more of the available actions as the recommended actions 184.”
Tennant_2020 and Jayan_2021 are analogous art to the claimed invention because they are from the same field of endeavor called energy modeling.
Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Tennant_2020 and Jayan_2021.
The rational for doing so would have been to follow teachings and motivation proposed in the prior art. Tennant_2020 discusses the invention in relation to isolated systems and buildings attached to the electric grid, without explicitly reciting the term “facility.” As understood by the specifications and implied by the claims (i.e.: a “building type” of the facility), the term facility and building are synonymous. Jayan_2021 explicitly recites the use of the invention for facilities. That is because “facilities” is the general term in the art used for organizational buildings. Furthermore, Jayan_2021 tracks this data explicitly to reduce carbon emissions, see jayan_2021 par 3: “One particular area in which organizations are focusing in their attempts to reduce carbon emissions is in controlling their use of non-renewable energy resources, such as coal, oil, natural gas, and the like.” One ordinarily skilled in the art would recognize that the invention of Tennant_2020 which models a difference in energy output between a renewable source of energy against the grid could be applied to facilities as outlined by Jayan_2021 as it would allow organizations to track their carbon output to allow them to reduce carbon emissions as could be required, see Jayan_2021 par 2: “various governing bodies and organizations are beginning to mandate that certain requirements be met by their members,” as well as performing actions as required to ensure those requirements are met.
Furthermore, in regards to the building type, the prior art of Tennant_2020 discusses the use of the invention in relation to isolated systems (like a boat) or buildings (like residential buildings). The prior art of Tennant_2020 bases this information on historical values of those buildings, but does not expressly recite including a building type in those calculations. Jayan_2021 does expressly recite the usage of building types when considering energy demand, see par 70: “the energy demand or generation may be forecasted based on building type or industry associated with the customers, local weather, building parameters (e.g., number of floors, area, typical occupancy, etc.). More accurately forecasting energy demand may enable the energy supplier to balance an electric grid using more renewable energy resources. “ It is understood by one ordinarily skilled in the art that if the invention of Tennant_2020 is applied to multiple building types or facilities, that one ordinarily skilled in the art would be motivated to modify the requirements of the invention to include building types as it would allow for more accurate forecasting energy demands.
Finally, in regards to the modeling. The prior art of Tennant_2020 discusses the use of models, but does not expressly recite that they are supervised machine learning models. The prior art of Jayan_2021 explicitly lists different potential models which could be used which encompass machine learning models. The motivation to combine would be “Obvious to try.” The prior art of Tennant_2020 already uses models which are trained. One ordinarily skilled in the art recognizes that machine learning models are either supervised or un-supervised learning models. One ordinarily skilled in the art would such as Tennant_2020 would have pursued solutions using either or models, and it would have been obvious to one ordinarily skilled in the art to used supervised machine learning methods as taught by Jayab_2021 for the goal of modeling the energy usage.
Therefore it would have been obvious to combine the energy comparison workflow of Tennant_2020 with the carbon monitoring for facilities using machine learning with building types of Jayan_2021 for the benefit of improving accuracy and applying the invention or organizational buildings for ensuring requirements and to obtain the invention as specified in the claims.
Jayan_2021 does not expressly recite executing a classification engine to identify a building type, the classification engine applying a k-nearest neighbor model configured to categorize a building type based on at least a building size, a building usage,
physics model
determining the building type for the facility with the classification engine;
Lingras_2020 makes obvious executing a classification engine to identify a building type, (par 30: “According to one embodiment, the goal of the clustering system 2000 is to deliver the closest possible building model 740 for a client building (Examiner note: building type) 860 (i.e., a building operated and/or owned by a client) based on that building's historical energy consumption data. The underlying assumption of clustering based on historical energy data is that energy data represents in and of itself an identifiable pattern unique to that type of building. For example, a large office building with having particular set of characteristics (such as building envelope including window-to-wall ratio, R and U factors of walls and windows, etc.) would resemble another large office building having similar characteristics, and may be distinguished from buildings with other characteristics (e.g., different building type (e.g., small office or hospital), different window-to-wall ratios, etc.).” the classification engine par 30: “ According to one embodiment, the goal of the clustering system 2000 is to deliver the closest possible building model 740 for a client building 860 (i.e., a building operated and/or owned by a client) based on that building's historical energy consumption data. The underlying assumption of clustering based on historical energy data is that energy data represents in and of itself an identifiable pattern unique to that type of building. For example, a large office building with having particular set of characteristics (such as building envelope including window-to-wall ratio, R and U factors of walls and windows, etc.) would resemble another large office building having similar characteristics, and may be distinguished from buildings with other characteristics (e.g., different building type (e.g., small office or hospital), different window-to-wall ratios, etc.).”) a building usage (par 41”The term “building occupancy” refers to categorizing structures based on their usage. Building occupancy classifications are usually defined by model building codes.”) , (Examiner note: where location demographic profile was mapped to Jayab_2021)
determining the building type for the facility with the classification engine; (par 30: “According to one embodiment, the goal of the clustering system 2000 is to deliver the closest possible building model 740 for a client building (Examiner note: building type) 860 (i.e., a building operated and/or owned by a client) based on that building's historical energy consumption data. The underlying assumption of clustering based on historical energy data is that energy data represents in and of itself an identifiable pattern unique to that type of building. For example, a large office building with having particular set of characteristics (such as building envelope including window-to-wall ratio, R and U factors of walls and windows, etc.) would resemble another large office building having similar characteristics, and may be distinguished from buildings with other characteristics (e.g., different building type (e.g., small office or hospital), different window-to-wall ratios, etc.).”)
Lingras_2020 is analogous art to the claimed invention because it is from the same field of endeavor called energy modeling. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Tennant_2020, Jayan_2021, and Lingras_2020. The rational for doing so would have been to follow a teaching and motivation proposed in the prior art. The prior arts of Tennant_2020 and Jayan_2021 both estimate energy requirements for buildings. The prior art of Lingras_2020 helps to classify buildings so that the energy requirements can be estimates more accurately. See par 6: “While existing BEMS/BAS systems require the above described inputs for real-time energy modeling, detailed information pertaining to building energy data including building envelope, building energy use and weather conditions as well as buildings occupancy is not maintained for most existing buildings. As such, the effort and time needed to perform AI-based building energy modeling based on detailed building energy related data is time consuming.” Therefore it would have been obvious to combine the energy modeling workflow which models energy usage of a building of Tennant_2020 with the building classification of Lingras_2020 for the benefit of saving time in energy data collection to obtain the invention as specified in the claims.
Lingras_2020 does not expressly recite applying a k-nearest neighbor model
physics model
Tuank_2019 however makes obvious applying a k-nearest neighbor model (abstract: “ K-Nearest Neighbours (KNN) is an effortless but productive machine learning algorithm. It is effective for classification as well as regression. However, it is more widely used for classification prediction. KNN groups the data into coherent clusters or subsets and classifies the new inputted data based on its similarity with previously trained data. The input is assigned to the class with which it shares the most nearest neighbours.”)
Tuank_2019 is analogous art to the claimed invention because it is from the same field of endeavor called machine learning. Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Lingras_2020 and Tuank_2019. The rational for doing so would have been applying a known technique to a known device to yield a predictable result. The prior art of Lingras_2020 teaches the use of clustering, see par 11: “and clustering each parameter-based group into a set of clusters, each cluster having a representative pattern, the energy profiles belonging to each cluster being sorted based on their proximity to the representative pattern; selecting a cluster from the set of clusters; and, selecting the energy profile in the cluster that is a closest match to that of the client building using the respective representative pattern for the cluster.” As stated by Tuank_2019, a KNN is a “widely used” method of classification using clustering, where one ordinarily skilled in the art would know to used the widely known technique of a KNN in the clustering algorithm of Lingras_2020 for building classification. Therefore it would have been obvious to combine the energy modeling workflow of Tennant_2020 that models energy usage of buildings of Lingras_2020 with the usage of KNN of Tuank_2020 for the usage of a known technique for the predictable result of clustering/classification.
Tuank_2019 does not expressly recite physics model
Vogt_2022 however makes obvious physics model (abstract “ We introduce a novel simulation tool capable of calculating the energy yield of a PV system (examiner note: solar energy, where this is a physics model) based on its fundamental material properties and using self-consistent models.”)
Tennant_2020 and Vogt_2022 are analogous art to the claimed invention because they are from the same field of endeavor called energy modeling.
Before the effective filing date, it would have been obvious to a person ordinarily skilled in the art to combine Tennant_2020 and Vogt_2022.
The rational for doing so would have been to follow a teaching and motivation proposed in the prior art. The art of Tennant_2020 teaches modeling renewable energy, as well as adjusting the output based on known characteristics of the specific installation. (par 117: ““ The user may adjust the formula parameters e.g. based on the known characteristics of their specific installation”). This directly matches the language used In the claim of the specifications, (par 64: the physical model may be used to scale this electrical output according to the dimensions of the solar panel, or according to the number of solar panels in a particular installation.” However, the prior art of Tennant_2020 does not expressly state that a physical model is performing this function. Vogt_2022 makes obvious that a physics model can be used for the purpose of predicting energy generation abstract: “ We introduce a novel simulation tool capable of calculating the energy yield of a PV system based on its fundamental material properties and using self-consistent models.” Which gives a benefit of abstract “operate without measurements of a PV device” Therefore it would have been obvious to combine the renewable energy estimation and parameter modification of Tennant_2020 with the physics model of Vogt_2022 for the benefit of simulating costume energy yield data without the need for measurements data to obtain the invention as specified in the claims.
Claim 2:The predictive analytics system of claim 1, further comprising code that performs the step of
Jayan_2021 makes further obvious comparing the carbon offset estimate for the facility to a carbon target for the facility, and generating a user recommendation based on a difference between the carbon offset estimate and the carbon target. (par 51: “The action recommendation engine 128 may select the recommended actions 184 to satisfy a particular criterion (e.g., environmental impact threshold) (Examiner note: Where a environmental impact threshold is understood to mean a carbon target) or to prioritize or optimize a particular criterion (e.g., total cost, environmental impact, etc.). For example, the action recommendation engine 128 may determine a net environmental impact estimate based on the respective environmental impacts that correspond to each of the selected energy resources 116, and the action recommendation engine 128 may compare the net environmental impact estimate to one or more thresholds. Depending on the relationship between the net environmental impact estimate and the one or more thresholds, the action recommendation engine 128 may select one or more of the available actions as the recommended actions 184.
Whereas previously stated, it would have been obvious to combine the energy comparison workflow of Tennant_2020 with the carbon monitoring for facilities using machine learning with building types of Jayan_2021 for the benefit of improving accuracy and applying the invention or organizational buildings to meet and ensure requirements (i.e.: targets) and to obtain the invention as specified in the claims.
Claim 3:The predictive analytics system of claim 1, further comprising code that performs the steps of
Tennent_2020 makes further obvious acquiring data from one or more sensors at the facility to monitor a current energy generation from the at least one colocated renewable energy source, par 70: “ For example, comparison of actual renewable energy generated with that predicted allows the user to adjust parameters to improve the accuracy of the weather prediction.” par 73: “ For example, renewable energy sources such as solar panels can be arranged into banks and can be connected in series or parallel or both. Each bank may have at least one sensor, for example to measure current and voltage. Similarly, energy storage devices may be grouped to form up to around three groups with any number of devices in each group connected in series or parallel. Each group may have at least one sensor, for example to measure temperature or current or voltage.”) and updating the carbon offset estimate for the interval based on the current energy generation. (Par 69: “The method may further comprise the step of measuring a performance of at least one renewable energy source and/or load, wherein the step of generating the prediction of energy demand and generating the prediction of renewable energy availability is performed using the or each measured performance. Measuring and comparing performances may provide feedback for the prediction generation, allowing the demand and renewable energy predictions to become more accurate and any decision making to become better informed. “) (Examiner note: Where the measured data being compared to the prediction data makes obvious updating an offset where this is a term in the offset equation).
Claim 4:The predictive analytics system of claim 1, further comprising code that performs the steps of
Tennant_2020 makes further obvious acquiring data from one or more sensors at the facility to monitor a current electrical consumption at the facility, and (par 138: “The real-time statuses 247 may further include values indicating the energy or power outputs of one or more of the sources 110, 120 and available energy or state of charge of one or more of the batteries 130, as well as the energy or power demand of one or more of the loads 140. A range of sensors may be used to detect, measure and/or relay the statuses 247 to a controller or processor.”)
updating the offset estimate for the interval based on the current electrical consumption. (Par 69: “The method may further comprise the step of measuring a performance of at least one renewable energy source and/or load, wherein the step of generating the prediction of energy demand and generating the prediction of renewable energy availability is performed using the or each measured performance. Measuring and comparing performances may provide feedback for the prediction generation, allowing the demand and renewable energy predictions to become more accurate and any decision making to become better informed. “) (Examiner note: Where the measured data being compared to the prediction data makes obvious updating an offset where this is a term in the offset equation).
Claim 5:
The predictive analytics system of claim 1,
Tennant_2020 makes further obvious wherein the colocated renewable energy source includes at least one of a solar power source or a wind turbine. (par 37: “The prediction of renewable energy availability may include an energy output value for each renewable energy source individually or in combination, over a predetermined time period, preferably 24 hours. The prediction of renewable energy availability may include a temporal series of energy output values for each renewable energy source individually or in combination, over a predetermined time period, preferably 24 hours. An energy output value for a solar panel and/or a weather parameter may be adjusted based on the time of day and/or an orientation of the solar panel.”)
Claim 6:
The claim limitations of claim 6 are effectively covered in claim 1, and are therefore rejected under a similar rational. Additionally, Tennent_2020 covers the unique limitations of A method for predictive analysis (abstract: “A method for managing an energy system having one or more renewable energy sources, one or more energy storage devices, one or more loads, and a grid connection for connecting at least temporarily to an external energy distribution grid is disclosed. The method generates a prediction of energy demand of the loads using historical energy demand data, and a prediction of renewable energy availability from the renewable energy sources using weather forecast data. An amount of energy to be obtained from the distribution grid is determined in dependence on the prediction of renewable energy availability and the prediction of energy demand. An energy conservation strategy is generated using the predictions and determined energy amount, and energy supplied to one or more of the energy storage devices and/or one or more of the loads is adjusted automatically according to the energy conservation strategy.”)
Claim 7:
The claim limitations of claim 7 are effectively covered in claim 2, and are therefore rejected under a similar rational.
Claim 8:
The method of claim 6, further comprising
Jayan_2021 makes further obvious displaying a comparison of the carbon offset estimate to the carbon target for the facility in a user interface. (par 63: “The end user application 206 may receive output of the data processing and ML models 204 (examiner note: the offset information). and may display a GUI that includes information for a user … “ par 96: “For example, the REC actions 812 may include recommendations to purchase various quantities of RECs based to achieve a target emissions offset.” Examiners note: Where it would be obvious to one ordinarily skilled in the art that the recommendations are displayed to a user, which makes obvious the target being displayed or inferred to the user. See Fig 7
Whereas previously stated, it would have been obvious to combine the energy comparison workflow of Tennant_2020 with the carbon monitoring for facilities using machine learning with building types of Jayan_2021 for the benefit of improving accuracy and applying the invention or organizational buildings to meet requirements (i.e.: targets) and to obtain the invention as specified in the claims.
Claim 9:
The method of claim 6, further comprising:
Tennent_2020 makes further obvious instrumenting the facility with a first one or more sensors to monitor a current energy generation from the at least one colocated renewable energy source at the facility; par 70: “ For example, comparison of actual renewable energy generated with that predicted allows the user to adjust parameters to improve the accuracy of the weather prediction.” par 73: “ For example, renewable energy sources such as solar panels can be arranged into banks and can be connected in series or parallel or both. Each bank may have at least one sensor, for example to measure current and voltage. Similarly, energy storage devices may be grouped to form up to around three groups with any number of devices in each group connected in series or parallel. Each group may have at least one sensor, for example to measure temperature or current or voltage.”)
and instrumenting the facility with a second one or more sensors to monitor a current electrical consumption at the facility. (par 138: “The real-time statuses 247 may further include values indicating the energy or power outputs of one or more of the sources 110, 120 and available energy or state of charge of one or more of the batteries 130, as well as the energy or power demand of one or more of the loads 140. A range of sensors may be used to detect, measure and/or relay the statuses 247 to a controller or processor.”)
Claim 10:
The method of claim 9, further comprising
Tennent_2020 makes further obvious updating the carbon offset estimate for the interval based on data from the first one or more sensors and the second one or more sensors during the interval. (Par 69: “The method may further comprise the step of measuring a performance of at least one renewable energy source and/or load, wherein the step of generating the prediction of energy demand and generating the prediction of renewable energy availability is performed using the or each measured performance. Measuring and comparing performances may provide feedback for the prediction generation, allowing the demand and renewable energy predictions to become more accurate and any decision making to become better informed. “) (Examiner note: Where the measured data being compared to the prediction data makes obvious updating an offset where this is a term in the offset equation).
Claim 11:
The method of claim 10, further comprising
Tennent_2020 makes further obvious presenting the updated carbon offset estimate to a user. (par 88: “ The system controller may be configured to send instructions to one or more of the controllers to activate one or more of the sensors, receive measured data from the sensors via the one or more buses, and store the measured data in the memory. The system controller may further comprise an analytic engine for performing analyses, wherein the analyses receive as input the predictions and/or data stored in memory. (Examiner note: the predictions and sensor data) The output of analyses may be stored in the memory. One or more of the analyses may be inputted by the user via the user interface. One or more of the analyses may output results to the user via the user interface. This may allow the user to perform custom analyses in order to evaluate the performance of the energy system according to their needs.”) Examiner note: Where an output of the analysis to a user via the user interface makes obvious presenting the updated carbon offset estimate.
Claim 12:
The method of claim 9, further comprising
Jayan_2021 makes further obvious normalizing and aggregating sensor data from the second one or more sensors for use in at least one of peer benchmarking and machine learning.
Par 84-85: “The method 600 includes creating a single dataset, at 602. For example, the computing device may fetch data from one or more sources of organization data associated with an organization that operates one or more facilities for which energy demand is to be forecast. The sources may include databases, servers, sensors, cameras, IoT devices, and the like. The data may be aggregated as needed to create the single data set, which may include sensor data, image data, headcount data, historic occupancy data, historical energy demand data, event and scheduling data, date/time information, seasonal data, employee data, energy supplier data, environmental impact data, government or regulatory agency data, and the like.
The method 600 includes imputing missing data, pre-processing the data, and performing feature engineering, at 604. Imputing missing data from the single dataset may be performed using extrapolation, gradient descent, or other techniques, as further described herein. Pre-processing the data may include converting the data to a common format, reducing the complexity of the data, or other pre-processing operations. The feature engineering may identify a subset of highly relevant features for training one or more ML models and discarding other features to reduce a complexity of training processes.
“ Examiner note: Where pre-processing the data above makes obvious normalization. Which is used in machine learning
Whereas previously stated, it would have been obvious to combine the energy comparison workflow of Tennant_2020 with the carbon monitoring for facilities using machine learning (using sensors) with building types of Jayan_2021 for the benefit of improving accuracy and applying the invention or organizational buildings to meet requirements (i.e.: targets) and to obtain the invention as specified in the claims.
Claim 13:
The method of claim 9,
Jayan_2021 makes obvious wherein the second one or more sensors include one or more device monitors within the facility. (par 43: “Although referred to as “sensor data.” the sensor data 170-172 may include measurements generated by sensors, image data generated by cameras, and other data generated by other devices that are located at the facilities 154-156. The sensor data 170-172 may be generated during performance of various monitoring operations by the sensors 140, the cameras 142, the IoT devices 144, the sensors 146, the cameras 148, and the IoT devices 150, such as scanning ID badges, monitoring areas of the facilities”) Examiner note: Where the one or more sensors includes device monitors in the facility.
Whereas previously stated, it would have been obvious to combine the energy comparison workflow of Tennant_2020 with the carbon monitoring for facilities using machine learning (using sensors) with building types of Jayan_2021 for the benefit of improving accuracy and applying the invention or organizational buildings to meet requirements (i.e.: targets) and to obtain the invention as specified in the claims.
Claim 14:The method of claim 6, wherein taking an action includes
Tennant_2020 makes obvious transmitting a request for a responsive action from a user (par 143: “Referring to FIG. 2b, embodiments of the method 200 may include a method for providing real-time alerts which will now be described. The real-time statuses 247 of the system 100 are provided to a step 270 of alerting the user 150. The user 150 is alerted via the user interface 160 if at least one of the real-time statuses 247 satisfies alert criteria 275. Each alert criterion 275 is indicative of particular conditions, including at least one threshold value, range of values and status category. The alert criteria 275 are classified into three levels of alert: critical, warning, and information.”)
Tennant_2020 does not expressly recite to a difference between the carbon offset estimate and a carbon target for the facility during the interval.
Jayan_2021 makes obvious to a difference between the carbon offset estimate and a carbon target for the facility during the interval.( par 51: “The action recommendation engine 128 may select the recommended actions 184 to satisfy a particular criterion (e.g., environmental impact threshold) (Examiner note: Where a environmental impact threshold is understood to mean a carbon target) or to prioritize or optimize a particular criterion (e.g., total cost, environmental impact, etc.). For example, the action recommendation engine 128 may determine a net environmental impact estimate based on the respective environmental impacts that correspond to each of the selected energy resources 116, and the action recommendation engine 128 may compare the net environmental impact estimate to one or more thresholds. Depending on the relationship between the net environmental impact estimate and the one or more thresholds, the action recommendation engine 128 may select one or more of the available actions as the recommended actions 184. )
Tennant_2020 makes obvious providing alerts due to thresholds, Jayan_2021 makes obvious conducting actions based on the thresholds (carbon offsets), where. One ordinarily skilled in the art would recognize that the alerts of Tennant_2020 based on thresh holds could be applied with carbon offset thresholds as stated by Jayan_2021. Therefore it would have been obvious to combine the alerts based on thresholds of Tennant_2020 with the carbon offset threshold of Jayan_2021 for the benefit of alerting a user of the real-time status of the system based on carbon offset information.
Claim 15:
The method of claim 6,
Tennant_2020 makes further obvious wherein taking an action includes generating an automated action par 14: “generating an energy conservation strategy using the predictions and determined energy amount; and adjusting energy supplied to one or more of the energy storage devices and/or one or more of the loads automatically according to the energy conservation strategy.”)
Tennant_2020 does not expressly recite reduce the carbon offset estimate
Jayan_2021 however makes obvious reduce the carbon offset estimate (par 51: “To illustrate recommending actions to offset the environmental impact corresponding to the selected energy resources 116, the action recommendation engine 128 may select, from multiple preset actions, one or more actions to be recommended to offset the environmental impact corresponding to the selected energy resources 116, such as planting a particular quantity of trees and purchasing a particular number of RECs based on selecting a first percentage of non-renewable/non-sustainable energy resources and a second percentage of renewable/sustainable energy resources to be included in the selected energy resources 116. Each of the available actions may be associated with a respective positive environmental impact, a respective cost, and the like, and such information may be used by the action recommendation engine 128 in addition to the selected energy resources 116 to select the recommended actions 184. The action recommendation engine 128 may select the recommended actions 184 to satisfy a particular criterion (e.g., environmental impact threshold) or to prioritize or optimize a particular criterion (e.g., total cost, environmental impact, etc.). For example, the action recommendation engine 128 may determine a net environmental impact estimate based on the respective environmental impacts that correspond to each of the selected energy resources 116, and the action recommendation engine 128 may compare the net environmental impact estimate to one or more thresholds. Depending on the relationship between the net environmental impact estimate and the one or more thresholds, the action recommendation engine 128 may select one or more of the available actions as the recommended actions 184.”)
Whereas previously stated in claim 1, it would have been obvious to combine the energy comparison workflow of Tennant_2020 with the carbon monitoring for facilities using machine learning with building types of Jayan_2021 for the benefit of improving accuracy and applying the invention or organizational buildings to meet and ensure requirements (i.e.: targets) and to obtain the invention as specified in the claims.
Claim 16:
The method of claim 6,
Tennant_2020 makes further obvious wherein the one or more result effective variables for the predictive model include at least one variable from the meteorological prediction data. (Par 117: “(par 117: “ The formulaic method provides the weather forecast data 206 as input to a formula, the formula parameterised using weather parameters, to produce the prediction 225 of renewable energy availability over the duration of the weather forecast data 206,”)
Claim 17:Claim 17 contains substantially similar limitations to claim 1 and is rejected under a similar rational. Additionally, the unique limitations are addressed in the previously cited prior art under claim 1 with similar motivations to combine:
Tennant_2020 makes obvious a physical site including a building (par 6 :” residential buildings”), a renewable energy source, (abstract: “renewable energy sources,”) and a plurality of sensors for monitoring energy usage at the building; (par 69: “The method may further comprise the step of measuring a performance of at least one renewable energy source and/or load, wherein the step of generating the prediction of energy demand and generating the prediction of renewable energy availability is performed using the or each measured performance. Measuring and comparing performances may provide feedback for the prediction generation, allowing the demand and renewable energy predictions to become more accurate and any decision making to become better informed. The method may further comprise the step of storing on a database the rated performances and/or one or more analyses, wherein the analyses receive as input one or more of the measured performances. The database may store data received from sensors in the energy system.”)
a database storing data acquired from the plurality of sensors; (par 69: “The database may store data received from sensors in the energy system.)
and a server hosting renewable energy management resources for the physical site, (these resources are the classification and predictive engines. See claim 1 for mapping)
a programmatic interface to a remote service configured to provide meteorological prediction data, (par 85: “The system controller may further comprise a network interface operable to communicate with a server via a data network (e.g. the Internet) and memory, wherein the system controller is operable to receive weather forecast data from the server via the network interface and store the weather forecast data in the memory. This can allow the system controller to receive up-to-date data with which to generate predictions. The system controller may be operable to receive updates such as software updates or component specifications.”)
Tennant_2020 does not expressly recite however Jayan_2021 makes obvious initiate, in response to a disparity between the expected output of the renewable source and the expected carbon production of the physical site, a responsive action for the physical site. Par 51: To illustrate recommending actions to offset the environmental impact corresponding to the selected energy resources 116, the action recommendation engine 128 may select, from multiple preset actions, one or more actions to be recommended to offset the environmental impact corresponding to the selected energy resources 116, such as planting a particular quantity of trees and purchasing a particular number of RECs based on selecting a first percentage of non-renewable/non-sustainable energy resources and a second percentage of renewable/sustainable energy resources to be included in the selected energy resources 116. Each of the available actions may be associated with a respective positive environmental impact, a respective cost, and the like, and such information may be used by the action recommendation engine 128 in addition to the selected energy resources 116 to select the recommended actions 184. The action recommendation engine 128 may select the recommended actions 184 to satisfy a particular criterion (e.g., environmental impact threshold) or to prioritize or optimize a particular criterion (e.g., total cost, environmental impact, etc.). For example, the action recommendation engine 128 may determine a net environmental impact estimate based on the respective environmental impacts that correspond to each of the selected energy resources 116, and the action recommendation engine 128 may compare the net environmental impact estimate to one or more thresholds. Depending on the relationship between the net environmental impact estimate and the one or more thresholds, the action recommendation engine 128 may select one or more of the available actions as the recommended actions 184.
Whereas previously stated in claim 1, it would have been obvious to combine the energy comparison workflow of Tennant_2020 with the carbon monitoring for facilities using machine learning with building types of Jayan_2021 for the benefit of improving accuracy and applying the invention or organizational buildings to meet and ensure requirements (i.e.: targets) and to obtain the invention as specified in the claims.
Claim 18:The system of claim 17,
Tennant_2020 makes obvious wherein the responsive action includes an automatic action by the server par 14: “generating an energy conservation strategy using the predictions and determined energy amount; and adjusting energy supplied to one or more of the energy storage devices and/or one or more of the loads automatically according to the energy conservation strategy.”)
Tennant_2020 does not expressly recite to reduce the disparity.
Jayan_2021 however makes obvious to reduce the disparity.(par 51: “To illustrate recommending actions to offset the environmental impact corresponding to the selected energy resources 116, the action recommendation engine 128 may select, from multiple preset actions, one or more actions to be recommended to offset the environmental impact corresponding to the selected energy resources 116, such as planting a particular quantity of trees and purchasing a particular number of RECs based on selecting a first percentage of non-renewable/non-sustainable energy resources and a second percentage of renewable/sustainable energy resources to be included in the selected energy resources 116. Each of the available actions may be associated with a respective positive environmental impact, a respective cost, and the like, and such information may be used by the action recommendation engine 128 in addition to the selected energy resources 116 to select the recommended actions 184. The action recommendation engine 128 may select the recommended actions 184 to satisfy a particular criterion (e.g., environmental impact threshold) or to prioritize or optimize a particular criterion (e.g., total cost, environmental impact, etc.). For example, the action recommendation engine 128 may determine a net environmental impact estimate based on the respective environmental impacts that correspond to each of the selected energy resources 116, and the action recommendation engine 128 may compare the net environmental impact estimate to one or more thresholds. Depending on the relationship between the net environmental impact estimate and the one or more thresholds, the action recommendation engine 128 may select one or more of the available actions as the recommended actions 184.”)
Whereas previously stated in claim 1, it would have been obvious to combine the energy comparison workflow of Tennant_2020 with the carbon monitoring for facilities using machine learning with building types of Jayan_2021 for the benefit of improving accuracy and applying the invention or organizational buildings to meet and ensure requirements (i.e.: targets) and to obtain the invention as specified in the claims.
Claim 19:
The system of claim 17,
Tennant_2020 makes obvious wherein the responsive action includes transmitting a notification par 143: “Referring to FIG. 2b, embodiments of the method 200 may include a method for providing real-time alerts which will now be described. The real-time statuses 247 of the system 100 are provided to a step 270 of alerting the user 150. The user 150 is alerted via the user interface 160 if at least one of the real-time statuses 247 satisfies alert criteria 275. Each alert criterion 275 is indicative of particular conditions, including at least one threshold value, range of values and status category. The alert criteria 275 are classified into three levels of alert: critical, warning, and information.”)
Tennant_2020 does not expressly recite concerning the disparity
Jayan_2021 makes obvious concerning the disparity .( par 51: “The action recommendation engine 128 may select the recommended actions 184 to satisfy a particular criterion (e.g., environmental impact threshold) (Examiner note: Where a environmental impact threshold is understood to mean a carbon target) or to prioritize or optimize a particular criterion (e.g., total cost, environmental impact, etc.). For example, the action recommendation engine 128 may determine a net environmental impact estimate based on the respective environmental impacts that correspond to each of the selected energy resources 116, and the action recommendation engine 128 may compare the net environmental impact estimate to one or more thresholds. Depending on the relationship between the net environmental impact estimate and the one or more thresholds, the action recommendation engine 128 may select one or more of the available actions as the recommended actions 184. )
Tennant_2020 makes obvious providing alerts due to thresholds, Jayan_2021 makes obvious conducting actions based on the thresholds (carbon offsets), where. One ordinarily skilled in the art would recognize that the alerts of Tennant_2020 based on thresh holds could be applied with carbon offset thresholds as stated by Jayan_2021. Therefore it would have been obvious to combine the alerts based on thresholds of Tennant_2020 with the carbon offset threshold of Jayan_2021 for the benefit of alerting a user of the real-time status of the system based on carbon offset information.
Claim 20:
The system of claim 17, Tennant_2020 makes obvious wherein the renewable energy source includes at least one of a solar power source and a wind turbine.
(par 37: “The prediction of renewable energy availability may include an energy output value for each renewable energy source individually or in combination, over a predetermined time period, preferably 24 hours. The prediction of renewable energy availability may include a temporal series of energy output values for each renewable energy source individually or in combination, over a predetermined time period, preferably 24 hours. An energy output value for a solar panel and/or a weather parameter may be adjusted based on the time of day and/or an orientation of the solar panel.”)
Conclusion
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/A.H.S./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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