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 .
Status of Claims
This action is in reply to application 18/534,043 filed 12/8/2023. Claims 1-20 are pending. This action is non-final.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: an energy simulation system and a machine-learning system in claims 1 and 19, and an off-grid energy generation system, an off-grid energy storage system, and an off-grid energy mobilization system in claim 10.
Because these claim limitations are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim limitations an energy simulation system and a machine-learning system in claims 1 and 19, and an off-grid energy generation system, an off-grid energy storage system, and an off-grid energy mobilization system in claim 10 invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Applicant’s specification does not provide clarification as to the structure of an energy simulation system and a machine-learning system in claims 1 and 19, and an off-grid energy generation system, an off-grid energy storage system, and an off-grid energy mobilization system in claim 10. In other words, the Applicant’s specification does not clarify whether an energy simulation system and a machine-learning system in claims 1 and 19, and an off-grid energy generation system, an off-grid energy storage system, and an off-grid energy mobilization system in claim 10 are hardware or software. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1 and 19 each recite a platform and a method for enabling intelligent orchestration and management of power and energy, comprising: a digital twin system including a digital twin of an energy operating asset, the digital twin of the energy operating asset including at least one of, at least one energy generation digital twin, at least one energy storage digital twin, at least one an energy delivery digital twin, or at least one energy consumption digital twin; an energy simulation system configured to generate a simulation of energy-related behavior of the energy operating asset; and a machine-learning system configured to generate a predicted state of the energy operating asset; wherein the simulation of energy-related behavior includes a simulation of carbon emissions of the energy operating asset based on at least one of, at least one historical pattern of the energy operating asset, at least one current state of the energy operating asset, or at least one predicted state of the energy operating asset. Therefore, claims 1 and 19 are each directed to one of the four statutory categories of invention: a machine and a method, respectively.
Step 2A – Prong One: The limitations ... enabling intelligent orchestration and management of power and energy, comprising: ... an energy operating asset ... the energy operating asset including at least one of, at least one energy generation ... at least one energy storage ... at least one an energy delivery ... or at least one energy consumption ... generate a simulation of energy-related behavior of the energy operating asset; and ... generate a predicted state of the energy operating asset; wherein the simulation of energy-related behavior includes a simulation of carbon emissions of the energy operating asset based on at least one of, at least one historical pattern of the energy operating asset, at least one current state of the energy operating asset, or at least one predicted state of the energy operating asset, as drafted, is a method that, under its broadest reasonable interpretation, only covers concepts of “Certain Methods of Organizing Human Activity” (e.g., commercial interactions – business relations). That is, nothing in the claim elements disclose anything outside the groupings of “Certain Methods of Organizing Human Activity” (e.g., commercial interactions – business relations). Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application. Claims 1 and 19 merely describe how to generally “apply” the concept of the aforementioned abstract idea using generic computer components. The additional elements of claims 1 and 19, an AI-based platform, a digital twin system, a digital twin of an energy operating asset, at least one energy generation digital twin, at least one energy storage digital twin, at least one an energy delivery digital twin, at least one energy consumption digital twin, and an energy simulation system, are recited at a high level of generality and are merely invoked as generic computer tools to perform the aforementioned abstract idea. Simply implementing the abstract idea on a generic computerized system is not a practical application of the abstract idea. Accordingly, alone and in combination, the additional elements of claims 1 and 19 do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claims as a whole merely describe the abstract idea generally “applied” to a generic computer environment. The additional elements of claims 1 and 19, an AI-based platform (described in spec. para. [0022]), a digital twin system (described in spec. para. [0111]), a digital twin of an energy operating asset (described in spec. para. [0045]), at least one energy generation digital twin (described in spec. para. [0045]), at least one energy storage digital twin (described in spec. para. [0045]), at least one an energy delivery digital twin (described in spec. para. [0045]), at least one energy consumption digital twin (described in spec. para. [0045]), and an energy simulation system (described in spec. para. [0036]), are recited at a high level of generality and are merely invoked as generic computer components upon which the abstract idea is “applied.” The high level of generality in which this additional element is described indicates that the additional element is sufficiently known such that the specification does not need to describe the particulars of the additional element to satisfy the statutory disclosure requirements. Thus, even when viewed as a whole, nothing in the claims add significantly more to the abstract idea. Therefore, the claims are not patent eligible.
Claims 2-18 and 20 have been given the full two-part analysis including analyzing the limitations both individually and in combination. Claims 2-18 and 20 when analyzed individually, and in combination, are also held to be patent ineligible under 35 U.S.C. 101. The recited limitations of the dependent claims fail to establish that the claims do not recite an abstract idea because the recited limitations of the dependent claims merely further narrow the abstract idea.
Step 2A – Prong Two: The limitations of the dependent claims fail to integrate an abstract idea into a practical application because the claims as a whole merely describe how to generally “apply” a method of the aforementioned abstract idea. Although claim 3 recites AI-based model and/or algorithm, a hardware system, and a software system, a supervised learning training process, a semi-supervised learning training process, claim 9 recites a distributed ledger and/or blockchain, and claim 12 recites retraining the digital twin, at least one other trained machine learning model, and a substitute digital twin, the claims as a whole merely describe how to generally “apply” the aforementioned abstract idea in a generic computer environment. Thus, even when viewed as a whole, nothing in the claims integrates the abstract idea into a practical application.
Step 2B: Performing the further narrowed abstract ideas of the dependent claims on the additional elements of the independent claim, individually or in combination, does not impose any meaningful limits on practicing the abstract ideas and amount to merely using a computer, in its ordinary capacity, as a tool to perform the abstract idea. Similarly, the recited limitations of the dependent claims fail to establish that the claims provide an inventive concept because claims that merely use a computer, in its ordinary capacity, as a tool to perform the abstract idea cannot provide an inventive concept. Although claim 3 recites AI-based model and/or algorithm (described in spec. para. [0029]), a hardware system (described in spec. para. [0029]), and a software system (described in spec. para. [0029]), a supervised learning training process (described in spec. para. [0029]), a semi-supervised learning training process (described in spec. para. [0029]), claim 9 recites a distributed ledger and/or blockchain (described in spec. para. [0034]), and claim 12 recites retraining the digital twin (described in spec. para. [0184]), at least one other trained machine learning model (described in spec. para. [0184]), and a substitute digital twin (described in spec. para. [0045]), they are recited at a high level of generality and are merely invoked as generic computer components upon which the abstract idea is “applied.” The high level of generality in which the additional elements are described indicates that the additional elements are sufficiently known such that the specification does not need to describe the particulars of the additional elements to satisfy the statutory disclosure requirements. Thus, even when viewed as a whole, nothing in the claims add significantly more to the abstract idea. Therefore, the claims are not patent eligible.
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 1-4 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Johnson (U.S. Pub. No. 2024/0266837) in view of Saurav (U.S. Pub. No. 2023/0153733).
Regarding claims 1 and 19, Johnson discloses the following limitations:
An ... platform for enabling intelligent orchestration and management of power and energy, comprising: [See [0007] Johnson teaches controlling one or more power system devices and/or distributed energy resources in an electrical power grid that includes simulating the electrical power grid in real-time in a digital twin simulation model, wherein the simulation model includes power system equipment and controller simulations. Johnson [0007] further teaches providing one or more inputs to the digital twin simulation model from one or more sensors in the electrical power grid, providing one or more inputs to a state estimator from the digital twin simulation model, the one or more inputs from the digital twin simulation model selected from the group consisting of active power, reactive power, voltage, current, frequency, power factor, or phasor data, and determining a state estimation solution at the state estimator that is provided to an optimization module (i.e., An ... platform for enabling intelligent orchestration and management of power and energy) that determines one or more control commands that are provided to the one or more power system equipment and controllers of the electrical power grid.]
a digital twin system including a digital twin of an energy operating asset [See [0007] Johnson teaches controlling one or more power system devices and/or distributed energy resources in an electrical power grid that includes simulating the electrical power grid in real-time in a digital twin simulation model (i.e., a digital twin system including a digital twin of an energy operating asset), wherein the simulation model includes power system equipment and controller simulations.]
the digital twin of the energy operating asset including at least one of, at least one energy generation digital twin, at least one energy storage digital twin, at least one an energy delivery digital twin, or at least one energy consumption digital twin; [See [0037-0038]; (Fig. 3); (Figs. 4A-4C) Johnson teaches implementing a digital twin concept to optimize the operations of an electrical distribution system (i.e., the digital twin of the energy operating asset). Johnson further teaches updating the optimization power system simulation with input coming from the physical power system into the digital twin. Johnson further teaches that the controllable power system equipment with associated digital twins may include a range of devices including DER devices (i.e., the digital twin of the energy operating asset including at least one of, at least one energy generation digital twin) and energy storage systems (i.e., the digital twin of the energy operating asset including at least one of ... at least one energy storage digital twin).]
an energy simulation system configured to generate a simulation of energy-related behavior of the energy operating asset; [See [0007] Johnson teaches controlling one or more power system devices and/or distributed energy resources in an electrical power grid that includes simulating the electrical power grid in real-time in a digital twin simulation model (i.e., an energy simulation system configured to generate a simulation of energy-related behavior of the energy operating asset), wherein the simulation model includes power system equipment and controller simulations.]
Johnson does not, however Saurav does, explicitly disclose the following limitations:
... AI-based platform ... and a machine-learning system configured to generate a predicted state of the energy operating asset; [See [0012]; [0037]; Saurav teaches generating greenhouse gas emission calculations using machine learning techniques (i.e., ... AI-based platform ... and a machine-learning system configured to generate a predicted state of the energy operating asset).]
wherein the simulation of energy-related behavior includes a simulation of carbon emissions of the energy operating asset based on at least one of, at least one historical pattern of the energy operating asset, at least one current state of the energy operating asset, or at least one predicted state of the energy operating asset. [See [0012]; [0037]; Saurav teaches generating greenhouse gas emission calculations using machine learning techniques (i.e., wherein the simulation of energy-related behavior includes a simulation of carbon emissions of the energy operating asset). Saurav [0013] further teaches training at least one machine learning model to learn relationships between physics-based simulations and/or models using cohort analytics to generate an estimate of energy (e.g., fuel) consumption, and using this energy consumption estimate to generate a modified and/or enhanced GHG emissions estimate (i.e., at least one predicted state of the energy operating asset) associated with a given supply chain-related transport context and/or portions thereof.]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the energy simulation system of Johnson with the energy simulation system of Saurav. By making this combination, the system of Johnson would be able to not just simulate energy generation, distribution, and consumption over an electrical grid, but also simulate the associated greenhouse gas emissions associated with the energy generation, distribution, and consumption. This would allow the system of Johnson to consider greenhouse gas factors (e.g., carbon credits) in its optimization methods. Furthermore, it would benefit the system of Johnson to utilize the machine learning data analysis techniques of Saurav.
Regarding claims 2 and 20, Johnson in view of Saurav discloses all claim 1 and 19 limitations. Johnson further discloses the following limitations:
wherein the digital twin is further configured to perform at least one of, providing at least one of a visual indicator or an analytic indicator of energy consumption by at least one energy consumer, filtering energy data, highlighting energy data, adjusting energy data, or generating at least one of a visual indicator or an analytic indicator of energy consumption by at least one of, at least one machine, at least one factory, or at least one vehicle in a vehicle fleet. [See [0037-0038]; (Fig. 3); (Figs. 4A-4C) Johnson teaches implementing a digital twin concept to optimize the operations of an electrical distribution system. Johnson further teaches updating the optimization power system simulation with input coming from the physical power system into the digital twin. Johnson further teaches that the controllable power system equipment with associated digital twins may include a range of devices including DER devices and energy storage systems Johnson [0038] further teaches that the power system-connected devices receive commands from the optimizer to change states or control parameters (i.e., wherein the digital twin is further configured to perform at least one of ... adjusting energy data ...).]
Regarding claim 3, Johnson in view of Saurav discloses all claim 1 limitations. Although Johnson teaches digital twin simulation techniques, Johnson does not, however Saurav does, explicitly disclose the following limitations:
at least one AI-based model and/or algorithm, wherein the at least one AI-based model and/or algorithm is trained based on a training data set, and the training data set is based on at least one of, at least one human tag, at least one label, at least one human interaction with a hardware system, at least one human interaction with a software system, at least one outcome, at least one AI-generated training data sample, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process. [See [0012]; [0037]; Saurav teaches generating greenhouse gas emission calculations using machine learning techniques. Saurav further teaches deriving contextual features from various forms of input data to profile drivers (i.e., at least one human tag, at least one label), routes and/or vehicles (i.e., at least one label) to identify one or more data cohorts, which can then be used to train at least one machine learning model to learn relationships between physics-based simulations to provide accurate GHG emissions estimates (i.e., at least one AI-based model and/or algorithm, wherein the at least one AI-based model and/or algorithm is trained based on a training data set, and the training data set is based on at least one of, at least one human tag, at least one label).]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the energy simulation system of Johnson with the energy simulation system of Saurav. By making this combination, the system of Johnson would be able to not just simulate energy generation, distribution, and consumption over an electrical grid, but also simulate the associated greenhouse gas emissions associated with the energy generation, distribution, and consumption. This would allow the system of Johnson to consider greenhouse gas factors (e.g., carbon credits) in its optimization methods. Furthermore, it would benefit the system of Johnson to utilize the machine learning data analysis techniques of Saurav.
Regarding claim 4, Johnson in view of Saurav discloses all claim 1 limitations. Johnson further discloses the following limitations:
wherein the digital twin is further configured to orchestrate a delivery of energy to at least one point of consumption, and the delivery of the energy includes at least one of, at least one fixed transmission line, at least one instance of wireless energy transmission, at least one delivery of fuel, or at least one delivery of stored energy. [See [0037-0038]; (Fig. 3); (Figs. 4A-4C) Johnson teaches implementing a digital twin concept to optimize the operations of an electrical distribution system. Johnson further teaches updating the optimization power system simulation with input coming from the physical power system into the digital twin. Johnson further teaches that the controllable power system equipment with associated digital twins may include a range of devices including DER devices and energy storage systems Johnson [0038] further teaches that the power system-connected devices receive commands from the optimizer to change states or control parameters (i.e., wherein the digital twin is further configured to orchestrate a delivery of energy). Johnson [0033] further teaches that the power systems controller includes a digital twin of the distribution power system module. Johnson further teaches that the distribution power system is a system that carries electricity from the transmission system to individual consumers (i.e., wherein the digital twin is further configured to orchestrate a delivery of energy to at least one point of consumption). Johnson [0025] further teaches that the power system comprises power lines (i.e., and the delivery of the energy includes at least one of, at least one fixed transmission line).]
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Johnson (U.S. Pub. No. 2024/0266837) in view of Saurav (U.S. Pub. No. 2023/0153733) in view of Abbey (U.S. Pub. No. 2021/0173969).
Regarding claim 5, Johnson in view of Saurav discloses all claim 1 and 4 limitations. Johnson in view of Saurav does not, however Abbey does, explicitly disclose the following limitations:
wherein the digital twin is further configured to adjust the delivery of energy to the at least one point of consumption based on at least one of an energy delivery policy or an energy consumption policy. [See [0121] Abbey teaches demand response policies in order to control energy consumption. For example, Abbey teaches the demand response policy definitions can specify which equipment can be turned on or off in response to particular demand inputs, how long a system or piece of equipment should be turned off (i.e., wherein the digital twin is further configured to adjust the delivery of energy to the at least one point of consumption based on at least one of an energy delivery policy or an energy consumption policy), what setpoints can be changed, what the allowable set point adjustment range is, how long to hold a high demand setpoint before returning to a normally scheduled setpoint, how close to approach capacity limits, which equipment modes to utilize, the energy transfer rates (e.g., the maximum rate, an alarm rate, other rate boundary information, etc.) into and out of energy storage devices (e.g., thermal storage tanks, battery banks, etc.), and when to dispatch on-site generation of energy (e.g., via fuel cells, a motor generator set, etc.).]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the energy control system of Johnson in view of Saurav with energy control system of Abbey. By making this combination, the system of Johnson in view of Saurav would be able to set predetermined policies which its automated energy control system would operate according to. This would allow the system of Johnson in view of Saurav to operate more efficiently and more autonomously, thereby saving on time required to manually adjust parameters.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Johnson (U.S. Pub. No. 2024/0266837) in view of Saurav (U.S. Pub. No. 2023/0153733) in view of White (U.S. Pub. No. 2024/0270569).
Regarding claim 6, Johnson in view of Saurav discloses all claim 1 and 4 limitations. Although Johnson teaches digital twin simulation techniques, Johnson in view of Saurav does not, however White does, explicitly disclose the following limitations:
... determine at least one of a carbon generation effect or a carbon emissions effect of the delivery of energy to the at least one point of consumption. [See [0025] White teaches calculating the amount of carbon dioxide associated with distributing hydrogen to consumers (i.e., ... determine at least one of a carbon generation effect or a carbon emissions effect of the delivery of energy to the at least one point of consumption).]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the emissions calculations of Johnson in view of Saurav with the emissions calculations of White. By making this combination, the system of Johnson in view of Saurav would be able to not just account for carbon emissions related to the production or consumption of energy, but also the distribution of it. This would provide the system of Johnson in view of Saurav with a more comprehensive view of the emissions produced by the generating, distributing, and consuming of their energy.
Claims 7-8 and 11-18 are rejected under 35 U.S.C. 103 as being unpatentable over Johnson (U.S. Pub. No. 2024/0266837) in view of Saurav (U.S. Pub. No. 2023/0153733) in view of Jones (U.S. Pub. No. 2022/0092346).
Regarding claim 7, Johnson in view of Saurav discloses all claim 1 and 4 limitations. Although Johnson teaches the digital twin adjusting the delivery of energy, Johnson in view of Saurav does not, however Jones does, explicitly disclose the following limitations:
adjust the delivery of energy to the at least one point of consumption based on a probability of a deficiency of available energy at the at least one point of consumption and a consequence of the deficiency of available energy at the at least one point of consumption. [See [0307] Jones teaches the power generation facility 1733 may dynamically scale power generation and power storage to compensate for projected increases or decreases in the demand. In some embodiments, the dynamic scaling may include an optimization function that minimizes power generation surplus while minimizing a risk of power generation deficiency. For example, the power generation facility 1733 may balance a cost of a surplus against a frequency or extent of a deficiency, thus ensuring that adequate power is generated without wasting resources. In some embodiments, the power generation facility 1733 may further adjust dynamic scaling where the extreme grid demand risk 1734 is high, such as, e.g., above 50%, above 60%, above 75% or other suitable threshold risk (i.e., adjust the delivery of energy to the at least one point of consumption based on a probability of a deficiency of available energy at the at least one point of consumption). For example, the power generation facility 1733 may generate and store an additional buffer of electrical power (e.g., using batteries or other power storage mechanism) where the risk of an extreme demand event is high. As a result, the power generation facility 1733 may improve grid power supply management to reduce the risk of a power deficiency while also reducing resource inefficiencies. Jones [0291] further teaches that a deficiency in electrical power supply may have drastic consequences including blackouts and brownouts that may limited to a given area or may be more widespread depending on the degree of the deficiency (i.e., and a consequence of the deficiency of available energy at the at least one point of consumption).]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the electrical grid management system of Johnson in view of Saurav with the electrical grid management system of Jones. By making this combination, the system of Johnson in view of Saurav would be able to “improve grid power supply management to reduce the risk of a power deficiency while also reducing resource inefficiencies.” See Jones [0307]. Furthermore, the system of Johnson in view of Saurav would be “advantageously trained to more accurately predict energy demand can provide improvements to power supply management and optimization to improve resource utilization efficiency and reduce power outages.” See Jones [0291].
Regarding claim 8, Johnson in view of Saurav discloses all claim 1 and 4 limitations. Although Johnson teaches the digital twin adjusting the delivery of energy, Johnson in view of Saurav does not, however Jones does, explicitly disclose the following limitations:
determine the delivery of energy to the at least one point of consumption based on a comparison of energy availability at each of two or more energy sources, wherein the comparison includes at least one of, a current quantity of energy stored by at least one of the two or more energy sources, a future quantity of energy stored by at least one of the two or more energy sources, a current resource expenditure associated with acquiring, storing, and/or delivering the energy by at least one of the two or more energy sources, a future resource expenditure associated with acquiring, storing, and/or delivering the energy by at least one of the two or more energy sources, a current demand by other energy consumers for the energy of at least one of the two or more energy sources, or a future demand by other energy consumers for the energy of at least one of the two or more energy sources. [See [0291] Jones teaches that a grid energy demand model 1708 may be trained to predict grid energy demand for power supply and storage (i.e., two or more energy sources) optimization. An excess of electrical power supply produced by a power generation facility may go unutilized, thus wasting the material, resources and money needed to supply the energy. Jones [0307] further teaches that in some embodiments, a power generation facility 1733 may receive the projected grid demand level 1732 and the extreme grid demand risk 1734 to optimize power generation. In some embodiments, the power generation facility 1733 may dynamically scale (i.e., determine the delivery of energy to the at least one point of consumption) power generation and power storage (i.e., energy availability at each of two or more energy sources) to compensate for projected increases or decreases in the demand (i.e., based on a comparison of energy availability at each of two or more energy sources, wherein the comparison includes at least one of ... a future demand by other energy consumers for the energy of at least one of the two or more energy sources).]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the electrical grid management system of Johnson in view of Saurav with the electrical grid management system of Jones. By making this combination, the system of Johnson in view of Saurav would be able to “improve grid power supply management to reduce the risk of a power deficiency while also reducing resource inefficiencies.” See Jones [0307]. Furthermore, the system of Johnson in view of Saurav would be “advantageously trained to more accurately predict energy demand can provide improvements to power supply management and optimization to improve resource utilization efficiency and reduce power outages.” See Jones [0291].
Regarding claim 11, Johnson in view of Saurav discloses all claim 1 limitations. Although Johnson teaches the digital twin adjusting the delivery of energy, Johnson in view of Saurav does not, however Jones does, explicitly disclose the following limitations:
measure a performance ... based on a prediction delta, the prediction delta is based on a comparison of a prediction generated ... based on a set of energy demand parameters with a measurement associated with energy operating asset, and the measurement corresponds to the prediction. [See [0270]; [0273]; Jones teaches outliers in the training dataset from the model request 1512 may reduce the accuracy (i.e., a prediction delta, the prediction delta is based on a comparison of a prediction generated ... based on a set of energy demand parameters with a measurement associated with energy operating asset, and the measurement corresponds to the prediction) of the implemented models, thus increasing the number of training iterations to achieve an accurate set of parameters for a given model in a given application. To improve accuracy and efficiency, the DOBR training engine 1501 may include a DOBR filter 1501b to dynamically test data point errors in the training dataset to determine outliers. Thus, outliers may be removed to provide a more accurate or representative of the training dataset from the model request 1512. In some embodiments the DOBR filter 1501b may provide an iterative mechanism for removing outlier data points subject to a pre-defined criterion, e.g., the user-define error acceptance value described above and provided, e.g., by a user via the user input device 1508. In some embodiments, the user-defined error acceptance value expressed as a percentage where, e.g., a value of 100% signifies that all of the error is accepted and no data points will be removed by the filter 1501b, while a value of, e.g., 0% results in all of the data points being removed. In some embodiments, the filter 1501b may be configured with an error acceptance value in the range of between, e.g., about 80% and about 95%.]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the electrical grid management system of Johnson in view of Saurav with the electrical grid management system of Jones. By making this combination, the system of Johnson in view of Saurav would be able to “improve grid power supply management to reduce the risk of a power deficiency while also reducing resource inefficiencies.” See Jones [0307]. Furthermore, the system of Johnson in view of Saurav would be “advantageously trained to more accurately predict energy demand can provide improvements to power supply management and optimization to improve resource utilization efficiency and reduce power outages.” See Jones [0291].
Regarding claim 12, Johnson in view of Saurav discloses all claim 1 limitations. Johnson in view of Saurav in view of Jones further teaches claim 11. Although Johnson teaches the digital twin adjusting the delivery of energy, Johnson in view of Saurav does not, however Jones does, explicitly disclose the following limitations:
update ... based on the prediction delta, and the updating includes at least one of, retraining ... based on the prediction delta, adjusting a prediction correction applied to predictions ... based on the prediction delta, supplementing ... with at least one other trained machine learning model, or replacing the digital twin with a substitute .... [See [0270]; [0273]; Jones teaches outliers in the training dataset from the model request 1512 may reduce the accuracy (i.e., the prediction delta) of the implemented models, thus increasing the number of training iterations to achieve an accurate set of parameters for a given model in a given application. To improve accuracy and efficiency, the DOBR training engine 1501 may include a DOBR filter 1501b to dynamically test data point errors in the training dataset to determine outliers. Thus, outliers may be removed to provide a more accurate or representative of the training dataset from the model request 1512. In some embodiments the DOBR filter 1501b may provide an iterative mechanism for removing outlier data points subject to a pre-defined criterion (i.e., update the ... based on the prediction delta, and the updating includes at least one of, retraining ... based on the prediction delta), e.g., the user-define error acceptance value described above and provided, e.g., by a user via the user input device 1508. In some embodiments, the user-defined error acceptance value expressed as a percentage where, e.g., a value of 100% signifies that all of the error is accepted and no data points will be removed by the filter 1501b, while a value of, e.g., 0% results in all of the data points being removed. In some embodiments, the filter 1501b may be configured with an error acceptance value in the range of between, e.g., about 80% and about 95%.]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the electrical grid management system of Johnson in view of Saurav with the electrical grid management system of Jones. By making this combination, the system of Johnson in view of Saurav would be able to “improve grid power supply management to reduce the risk of a power deficiency while also reducing resource inefficiencies.” See Jones [0307]. Furthermore, the system of Johnson in view of Saurav would be “advantageously trained to more accurately predict energy demand can provide improvements to power supply management and optimization to improve resource utilization efficiency and reduce power outages.” See Jones [0291].
Regarding claim 13, Johnson in view of Saurav discloses all claim 1 limitations. Although Johnson teaches the digital twin adjusting the delivery of energy, Johnson in view of Saurav does not, however Jones does, explicitly disclose the following limitations:
generate, a prediction based on at least one energy demand parameter, and an indication of an effect of the at least one energy demand parameter on the prediction. [See [0291] Jones teaches that a grid energy demand model 1708 may be trained to predict grid energy demand for power supply and storage (i.e., generate, a prediction based on at least one energy demand parameter) optimization. An excess of electrical power supply produced by a power generation facility may go unutilized, thus wasting the material, resources and money needed to supply the energy (i.e., an indication of an effect of the at least one energy demand parameter on the prediction). Jones [0307] further teaches that in some embodiments, a power generation facility 1733 may receive the projected grid demand level 1732 and the extreme grid demand risk 1734 to optimize power generation. In some embodiments, the power generation facility 1733 may dynamically scale to compensate for projected increases or decreases in the demand.]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the electrical grid management system of Johnson in view of Saurav with the electrical grid management system of Jones. By making this combination, the system of Johnson in view of Saurav would be able to “improve grid power supply management to reduce the risk of a power deficiency while also reducing resource inefficiencies.” See Jones [0307]. Furthermore, the system of Johnson in view of Saurav would be “advantageously trained to more accurately predict energy demand can provide improvements to power supply management and optimization to improve resource utilization efficiency and reduce power outages.” See Jones [0291].
Regarding claim 14, Johnson in view of Saurav discloses all claim 1 limitations. Although Johnson teaches the digital twin adjusting the delivery of energy, Johnson in view of Saurav does not, however Jones does, explicitly disclose the following limitations:
determine at least one modification of a set of energy demand parameters to improve future predictions of the digital twin, wherein the at least one modification include at least one of, at least one additional historical, current, and/or forecast energy demand parameter associated with a set of fixed entities and a set of mobile entities within a defined domain, or at least one modification of the at least one additional historical, current, and/or forecast energy demand parameter associated with the set of fixed entities and the set of mobile entities within the defined domain. [See [0270]; [0273]; Jones teaches outliers in the training dataset from the model request 1512 may reduce the accuracy of the implemented models, thus increasing the number of training iterations to achieve an accurate set of parameters for a given model in a given application. To improve accuracy and efficiency, the DOBR training engine 1501 may include a DOBR filter 1501b to dynamically test data point errors in the training dataset to determine outliers. Thus, outliers may be removed to provide a more accurate or representative of the training dataset from the model request 1512. In some embodiments the DOBR filter 1501b may provide an iterative mechanism for removing outlier data points subject to a pre-defined criterion, e.g., the user-define error acceptance value described above and provided, e.g., by a user via the user input device 1508. In some embodiments, the user-defined error acceptance value expressed as a percentage where, e.g., a value of 100% signifies that all of the error is accepted and no data points will be removed by the filter 1501b, while a value of, e.g., 0% results in all of the data points being removed. In some embodiments, the filter 1501b may be configured with an error acceptance value in the range of between, e.g., about 80% and about 95%. Jones [0339] further teaches determining a set of updated model parameters for the machine learning model based on the non-outlier data set (i.e., determine at least one modification of a set of energy demand parameters to improve future predictions of the digital twin, wherein the at least one modification include at least one of, at least one additional historical, current, and/or forecast energy demand parameter associated with a set of fixed entities and a set of mobile entities within a defined domain).]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the electrical grid management system of Johnson in view of Saurav with the electrical grid management system of Jones. By making this combination, the system of Johnson in view of Saurav would be able to “improve grid power supply management to reduce the risk of a power deficiency while also reducing resource inefficiencies.” See Jones [0307]. Furthermore, the system of Johnson in view of Saurav would be “advantageously trained to more accurately predict energy demand can provide improvements to power supply management and optimization to improve resource utilization efficiency and reduce power outages.” See Jones [0291].
Regarding claim 15, Johnson in view of Saurav discloses all claim 1 limitations. Although Johnson teaches the digital twin adjusting the delivery of energy, Johnson in view of Saurav does not, however Jones does, explicitly disclose the following limitations:
orchestrate a delivery of energy to at least one point of consumption based on at least one entity parameter received from at least one entity of a set of fixed entities and/or a set of mobile entities within a defined domain, and the at least one entity parameter includes at least one of, a current energy status of the at least one entity, a future energy status of the at least one entity, a current energy consumption by the at least one entity, a future energy consumption by the at least one entity, a current activity performed by the at least one entity that is associated with energy consumption, or a future activity performed by the at least one entity that is associated with energy consumption. [See [0291] Jones teaches that a grid energy demand model 1708 may be trained to predict grid energy demand for power supply and storage optimization. An excess of electrical power supply produced by a power generation facility may go unutilized, thus wasting the material, resources and money needed to supply the energy. Jones [0307] further teaches that in some embodiments, a power generation facility 1733 may receive the projected grid demand level 1732 and the extreme grid demand risk 1734 to optimize power generation. In some embodiments, the power generation facility 1733 may dynamically scale to compensate for projected increases or decreases in the demand (i.e., orchestrate a delivery of energy to at least one point of consumption based on at least one entity parameter received from at least one entity of a set of fixed entities and/or a set of mobile entities within a defined domain, and the at least one entity parameter includes at least one of ... a future energy consumption by the at least one entity).]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the electrical grid management system of Johnson in view of Saurav with the electrical grid management system of Jones. By making this combination, the system of Johnson in view of Saurav would be able to “improve grid power supply management to reduce the risk of a power deficiency while also reducing resource inefficiencies.” See Jones [0307]. Furthermore, the system of Johnson in view of Saurav would be “advantageously trained to more accurately predict energy demand can provide improvements to power supply management and optimization to improve resource utilization efficiency and reduce power outages.” See Jones [0291].
Regarding claim 16, Johnson in view of Saurav discloses all claim 1 limitations. Although Johnson teaches the digital twin adjusting the delivery of energy, Johnson in view of Saurav does not, however Jones does, explicitly disclose the following limitations:
transmit, to at least one entity of a set of fixed entities and/or a set of mobile entities within a defined domain, a request to adjust at least one entity parameter associated with the at least one entity, and the at least one entity parameter includes at least one of, a current energy status of the at least one entity, a future energy status of the at least one entity, a current energy consumption by the at least one entity, a future energy consumption by the at least one entity, a current activity performed by the at least one entity that is associated with energy consumption, or a future activity performed by the at least one entity that is associated with energy consumption. [See [0291] Jones teaches that a grid energy demand model 1708 may be trained to predict grid energy demand for power supply and storage optimization. An excess of electrical power supply produced by a power generation facility may go unutilized, thus wasting the material, resources and money needed to supply the energy. Jones [0307] further teaches that in some embodiments, a power generation facility 1733 may receive the projected grid demand level 1732 and the extreme grid demand risk 1734 to optimize power generation. In some embodiments, the power generation facility 1733 may dynamically scale to compensate for projected increases or decreases in the demand (i.e., transmit, to at least one entity of a set of fixed entities and/or a set of mobile entities within a defined domain, a request to adjust at least one entity parameter associated with the at least one entity, and the at least one entity parameter includes at least one of ... a future energy consumption by the at least one entity).]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the electrical grid management system of Johnson in view of Saurav with the electrical grid management system of Jones. By making this combination, the system of Johnson in view of Saurav would be able to “improve grid power supply management to reduce the risk of a power deficiency while also reducing resource inefficiencies.” See Jones [0307]. Furthermore, the system of Johnson in view of Saurav would be “advantageously trained to more accurately predict energy demand can provide improvements to power supply management and optimization to improve resource utilization efficiency and reduce power outages.” See Jones [0291]
Regarding claim 17, Johnson in view of Saurav discloses all claim 1 limitations. Johnson further discloses the following limitations:
wherein the digital twin is further configured to, perform a simulation of at least one process of at least one physical machine associated with at least one of a set of fixed entities or a set of mobile entities [See [0007] Johnson teaches controlling one or more power system devices and/or distributed energy resources in an electrical power grid that includes simulating the electrical power grid in real-time in a digital twin simulation model, wherein the simulation model includes power system equipment and controller simulations (i.e., wherein the digital twin is further configured to, perform a simulation of at least one process of at least one physical machine associated with at least one of a set of fixed entities or a set of mobile entities).]
Johnson in view of Saurav does not, however Jones does, explicitly disclose the following limitations:
and output at least one energy demand parameter resulting from the at least one process based on the simulation. [See [0251] Jones teaches that a simulator may perform a simulation of a work operation of the machine based on the control command. The simulator may generate a complete data set for training the machine learning model by simulating physical actions of the machine based on the control command. Such a dataset may be processed using the DOBR iterations to ensure any outlier simulations are removed when training the model parameters including the work operation data, control command data and machine data used as input for each simulation. Jones [0291] further teaches that a grid energy demand model 1708 may be trained to predict grid energy demand for power supply and storage optimization. An excess of electrical power supply produced by a power generation facility may go unutilized, thus wasting the material, resources and money needed to supply the energy. Jones [0307] further teaches that in some embodiments, a power generation facility 1733 may receive the projected grid demand level 1732 and the extreme grid demand risk 1734 to optimize power generation. In some embodiments, the power generation facility 1733 may dynamically scale to compensate for projected increases or decreases in the demand (i.e., and output at least one energy demand parameter resulting from the at least one process based on the simulation).]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the electrical grid management system of Johnson in view of Saurav with the electrical grid management system of Jones. By making this combination, the system of Johnson in view of Saurav would be able to “improve grid power supply management to reduce the risk of a power deficiency while also reducing resource inefficiencies.” See Jones [0307]. Furthermore, the system of Johnson in view of Saurav would be “advantageously trained to more accurately predict energy demand can provide improvements to power supply management and optimization to improve resource utilization efficiency and reduce power outages.” See Jones [0291]
Regarding claim 18, Johnson in view of Saurav discloses all claim 1 limitations. Johnson further discloses the following limitations:
wherein the digital twin is associated with at least one physical machine associated with at least one of a set of fixed entities or a set of mobile entities [See [0007] Johnson teaches controlling one or more power system devices and/or distributed energy resources in an electrical power grid that includes simulating the electrical power grid in real-time in a digital twin simulation model, wherein the simulation model includes power system equipment and controller simulations (i.e., wherein the digital twin is further configured to, perform a simulation of at least one process of at least one physical machine associated with at least one of a set of fixed entities or a set of mobile entities).]
Johnson in view of Saurav does not, however Jones does, explicitly disclose the following limitations:
updated by the AI-based platform to generate output of a process that corresponds to an updated detection of output of the process performed by the at least one physical machine. [See [0251] Jones teaches that a simulator may perform a simulation of a work operation of the machine based on the control command. The simulator may generate a complete data set for training the machine learning model by simulating physical actions of the machine based on the control command. Such a dataset may be processed using the DOBR iterations to ensure any outlier simulations are removed when training the model parameters including the work operation data, control command data and machine data used as input for each simulation. Jones [0291] further teaches that a grid energy demand model 1708 may be trained to predict grid energy demand for power supply and storage optimization. An excess of electrical power supply produced by a power generation facility may go unutilized, thus wasting the material, resources and money needed to supply the energy. Jones [0307] further teaches that in some embodiments, a power generation facility 1733 may receive the projected grid demand level 1732 and the extreme grid demand risk 1734 to optimize power generation. In some embodiments, the power generation facility 1733 may dynamically scale to compensate for projected increases or decreases in the demand (i.e., updated by the AI-based platform to generate output of a process that corresponds to an updated detection of output of the process performed by the at least one physical machine).]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the electrical grid management system of Johnson in view of Saurav with the electrical grid management system of Jones. By making this combination, the system of Johnson in view of Saurav would be able to “improve grid power supply management to reduce the risk of a power deficiency while also reducing resource inefficiencies.” See Jones [0307]. Furthermore, the system of Johnson in view of Saurav would be “advantageously trained to more accurately predict energy demand can provide improvements to power supply management and optimization to improve resource utilization efficiency and reduce power outages.” See Jones [0291]
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Johnson (U.S. Pub. No. 2024/0266837) in view of Saurav (U.S. Pub. No. 2023/0153733) in view of Sarker (U.S. Pub. No. 2020/0051186).
Regarding claim 9, Johnson in view of Saurav discloses all claim 1 and 4 limitations. Although Johnson teaches the digital twin adjusting the delivery of energy, Johnson in view of Saurav does not, however Sarker does, explicitly disclose the following limitations:
record, in a distributed ledger and/or blockchain, at least one energy-related event, the at least one energy- related event including at least one of, an energy purchase event, an energy sale event, a service charge associated with an energy purchase event, a service charge associated with an energy sale event, an energy consumption event, an energy generation event, an energy distribution event, an energy storage event, a carbon emission production event, a carbon emission abatement event, a renewable energy credit event, a pollution production event, or a pollution abatement event. [See [0063] Sarker teaches the exchange of renewable energy credits may be recorded in real-time as transactions in a blockchain (i.e., record, in a distributed ledger and/or blockchain, at least one energy-related event, the at least one energy- related event including at least one of ... a renewable energy credit event).]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the emissions calculating system of Johnson in view of Saurav with the renewable energy credit recording system of Sarker. By making this combination, an administrator of the system of Johnson in view of Saurav would be able to easily view going rates for renewable energy credits on a blockchain, and also easily buy and sell them according to their needs.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Johnson (U.S. Pub. No. 2024/0266837) in view of Saurav (U.S. Pub. No. 2023/0153733) in view of Tural (U.S. Pub. No. 2022/0190597).
Regarding claim 10, Johnson in view of Saurav discloses all claim 1 limitations. Although Johnson teaches the digital twin adjusting the delivery of energy, Johnson in view of Saurav does not, however Tural does, explicitly disclose the following limitations:
an off-grid environment, and the off-grid environment includes at least one of, an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system. [See [0001] Tural teaches a management system for managing off-grid energy generation (i.e., an off-grid energy generation system) and storage (i.e., an off-grid energy storage system).]
It would have been obvious to one of ordinary skill in the art before the time of filing to combine the energy management system of Johnson in view of Saurav with the energy management system of Tural. By making this combination, the benefits of the system of Johnson in view of Saurav would be able to be applied to small-scale use in off-grid environments. This would allow users the flexibility of using such an energy management system to control a residential solar panel system at their home, for example.
Prior Art
The following prior art is relevant to the invention but was not used in prior art rejections:
Taheri (U.S. Pub. No. 2023/0324860) – Estimating energy consumption for a building using dilated convolutional neural networks.
B R (U.S. Pub. No. 2023/0061681) – Method and system for determining optimal computing configuration for executing computing operation.
Widyaratne (U.S. Pub. No. 2024/0346521) – System and method for token-based trading of carbon credits.
Wang (U.S. Pub. No. 2021/0201185) – Environmental state analysis method, and user terminal and non-transitory medium implementing same.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRIS GOMEZ whose telephone number is (571) 272-0926. The examiner can normally be reached on 7:30 AM – 4:30 PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SHANNON CAMPBELL can be reached at (571) 272-5587. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTOPHER GOMEZ/ Examiner, Art Unit 3628
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