DETAILED ACTION
Status of Claims
This Final Office Action is responsive to Applicant's reply filed 10/23/2025.
Claims 1, 4-10, 12-14, and 17-20 have been amended and claims 2 and 15 have been cancelled.
Claims 1, 3-14, and 16-20 are currently pending and have been examined.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/5/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Amendments
The previously pending specification objection has been withdrawn in response to Applicant’s amendment.
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 103 and 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
Claim 20 is rejected under 35 USC 101 for being directed towards non-statutory subject matter (e.g. computer readable media). Appropriate correction is required.
With regard to the limitations of claims 1, 3-14, and 16-20, Applicant argues that the claims are not directed towards a mental process. The Examiner notes the claims are directed towards Organizing Human Activity. The Examiner points to the rejection below. Generically implementing an abstract idea on a general purpose computer does not make the claims eligible (See MPEP 2106). Applicant’s arguments are not persuasive.
Applicant makes citations to the specification pointing out examples of how the claims can be used, but the claims recite generic control of node controllers with no definition of what the node controller entails or how it is even controlled, which is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106). Applicant’s arguments are not persuasive.
Claim 20 is rejected under 35 USC 101 for being directed towards non-statutory subject matter (e.g. computer program product). Applicant’s specification makes no definition of a computer program product. The Examiner strongly recommends just amending the claim to recite non-transitory to solve the extremely simple issue. Appropriate correction is required. Applicant’s arguments are not persuasive.
With regard to the limitations of claims 1, 3-14, and 16-20, Applicant argues that the claims are allowable over 35 USC 103 because the claim amendments overcome the current art rejection. The Examiner respectfully disagrees. Please see the updated rejection below since amendments by Applicant require additional reference to the Examiner’s art rejection.
Applicant argues Hannon does not disclose an amount of energy drawn at a node. The Examiner respectfully disagrees. Hannon Paragraph 0159 – “dynamic allocation flexibility associated with the a respective energy device … evaluating dynamic future allocation with future energy execution prediction, wherein the dynamic future allocation includes at least energy operational information based on a categorization of energy usage at a plurality of respective energy devices, and trigger energy generation on the energy grid at respective generator nodes according to the learning model and dynamic projections”, where the energy generation trigger is based on each individual node based on the analysis of the whole grid and each node. Applicant’s arguments are not persuasive.
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, 3-14, and 16-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 1 and 2-13 are directed toward a process and claims 14 and 16-19 are directed toward a system; which are statutory categories of invention. Claim 20 is directed towards a computer readable media, which is NOT a statutory category of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward a computer implemented method comprising: training a machine learning (ML) model based on historic time series data, wherein the historic time series data is associated with one of more energy source nodes and one or more energy sink nodes; capturing, via one or more sensors associated with each of the one or more energy source nodes real time power generation data of each of the one or more energy source nodes, the one or more energy source nodes are configured to generate energy; capturing, via one or more sensors associated with each of the one or more energy sink nodes, real time power consumption data of the one or more energy sink nodes, the one or more energy sink nodes are configured to consume the generated energy; obtaining, by the trained ML model, predicted power consumption data for the one or more energy sink nodes; obtaining, by the trained ML model, predicted power generation data for each of the one or more energy source nodes; determining, by the trained ML model, predicted allocation data corresponding to at least one energy source node of the one or more energy source nodes based on at least one of: the predicted power consumption data, or the predicted power generation data, the predicted allocation data comprising: identification data of the at least one energy source node from the one or more energy source nodes, and an energy amount to be drawn from the at least one energy source node associated with the identification data; determining actual allocation data corresponding to the at least one energy source node of the one or more energy source nodes based on the real time power consumption data, the real time power generation data, the predicted power consumption data, and the predicted power generation data; generating, based on the actual allocation data, a first control signal for the at least one energy source node associated with the identification data; and controlling, based on the first control signal, a first energy node controller, of a plurality of energy node controllers, connected to the at least one energy source node to distribute the energy amount from the at least one energy source node to the one or more energy sink nodes (Organizing Human Activity), which are considered to be abstract ideas (See MPEP 2106.05). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are comparing power generation data to consumption data to help allocate energy based on predicted usages and generations for optimization purposes using a generic machine learning model and telling a human how to control the system using a controller, which is a commercial interaction.
Dependent claims 3-13 and 16-19 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below.
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the Independent claims additionally recite “capturing, via one or more sensors associated with each of the one or more energy source nodes real time power generation data of each of the one or more energy source nodes; capturing, via one or more sensors associated with each of the one or more energy sink nodes, real time power consumption data of the one or more energy sink nodes (claims 1, 14, and 20)”, which would not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data merely add insignificant extra-solution activity and merely adds the words to apply it with the judicial exception. Also, the Independent claims further recite “computer; energy source node; energy sink node; machine learning model; one or more sensors associated with each of the one or more energy sink nodes (claim 1)”; “system, comprising: processing circuitry configured to (claim 14)”; “computer program product for energy flow control, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executed by a system to cause the system to (claim 20)”, which are additional elements that would not integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
The Examiner asserts the claimed training and using a machine learning model are recited at such a high level of generality that they merely add the words apply it with the judicial exception (See MPEP 2106).
In addition, dependent claims 3-13 and 16-19 further narrow the abstract idea and dependent claims 8-9 additionally recite “capturing, via one or more sensors associated with each of the one or more energy sink nodes energy sink data (claim 8); capturing, via one or more sensors associated with each of the one or more energy source nodes energy source data (claim 9)” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because receiving/storing data merely add insignificant extra-solution activity and the claimed “one or more sensors (claims 8 and 9)” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer because it is recited at such a high level of generality (See MPEP 2106.05).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05). Further, method; System; and Product Independent claims 1, 3-14, and 16-20 recite computer; energy source node; energy sink node; machine learning model; one or more sensors associated with each of the one or more energy sink nodes (claim 1)”; “system, comprising: processing circuitry configured to (claim 14)”; “computer program product for energy flow control, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executed by a system to cause the system to (claim 20); however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0017-0019 and Figures 1. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. Also, the claimed “capturing, via one or more sensors associated with each of the one or more energy source nodes real time power generation data of each of the one or more energy source nodes; capturing, via one or more sensors associated with each of the one or more energy sink nodes, real time power consumption data of the one or more energy sink nodes (claims 1, 14, and 20)” steps/functions of the independent claims would not account for significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 3-13 and 16-19 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 8-9 additionally recite “capturing, via one or more sensors associated with each of the one or more energy sink nodes energy sink data (claim 8); capturing, via one or more sensors associated with each of the one or more energy source nodes energy source data (claim 9)” which do not account for additional elements that amount to significantly more than the abstract idea because receiving data and displaying/presenting data (See MPEP 2106.05) have been identified as well-known, routine, and conventional steps/functions to one of ordinary skill in the art and the claimed “one or more sensors (claims 8 and 9)” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed structure merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claims 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 20 recites “a computer program product” where the broadest reasonable interpretation of a claim drawn to a computer readable media covers the forms of non-transitory tangible medial and transitory propagating signal per se when the specification is silent. See MPEP 2111.01. Since the claims are drawn to a computer program product and the specification is silent, the claims are rejected under 35 U.S.C. 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed cir 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C 101, Aug 24, 2009; p. 2. 7. Applicant advised to amend the claim to recite "non-transitory computer readable media” to overcome rejection under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3-14, and 16-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hannon (US 2023/0061173 A1) in view of Matan et al. (US 2021/0328456 A1).
Regarding Claim 1: Hannon teaches a computer implemented method comprising (See Figure 4, Abstract, and claim 31):
training a machine learning (ML) model based on historic time series data, wherein the historic time series data is associated with one of more energy source nodes and one or more energy sink nodes (See Figure 5, Paragraph 0021 – “generate a learning model for evaluating dynamic future allocation with future energy execution prediction, wherein the dynamic future allocation includes at least energy operational information based on a categorization of energy usage at a plurality of respective energy devices, and trigger energy generation on the energy grid at respective generator nodes according to the learning model and dynamic projections”, Paragraph 0040, Paragraph 0120 – “a machine learning model is trained on existing customer data to enable end user devices to be classified automatically into categories of use with associated use preference”, and Paragraph 0162);
capturing power generation data for each of one or more energy source nodes, the one or more energy source nodes are configured to generate energy (See Figure 1, Figure 8, Paragraph 0088 – “generation or load, and next-day or real-time (hourly) time period”, Paragraph 0090 – “load or generation, zonal data or individual nodes”, Paragraph 0116, Paragraph 0153 – “generation/storage capacity”, and claim 1);
capturing real time power consumption data of the one or more energy sink nodes, the one or more energy sink nodes are configured to consume the generated energy (See Figure 8 – “802, 804”, Figure 30, Paragraph 0086, Paragraph 0088 – “generation or load, and next-day or real-time (hourly) time period”, Paragraph 0090 – “load or generation, zonal data or individual nodes”, Paragraph 0116, Paragraph 0140 – “determine same day hourly consumption”, and claim 1);
obtaining, by the trained ML model, predicted power consumption data for the one or more energy sink nodes (See Paragraph 0086 – “model an hourly consumption estimate (load profile)”, Paragraph 0095, Paragraph 0096 – “Energy Management System anticipates demand of all devices”, Paragraph 0097, and Paragraph 0136);
obtaining, by the trained ML model, predicted power generation data for each of the one or more energy source nodes (See Figure 1, Paragraph 0095 – “forecast energy needs (e.g., generation, consumption, time-shifting, etc.)”, Paragraph 0097, Paragraph 0101 – “data about capacity”, and Paragraph 0125);
determining, by the trained ML model, predicted allocation data corresponding to at least one energy source node of the one or more energy source nodes based on at least one of: the predicted power consumption data, or the predicted power generation data, the predicted allocation data comprising: identification data of the at least one energy source node of the one or more energy source nodes, and an energy amount to be drawn from the at least one energy source node associated with the identification data (See Paragraph 0159 – “dynamic allocation flexibility associated with the a respective energy device … evaluating dynamic future allocation with future energy execution prediction, wherein the dynamic future allocation includes at least energy operational information based on a categorization of energy usage at a plurality of respective energy devices, and trigger energy generation on the energy grid at respective generator nodes according to the learning model and dynamic projections”, Paragraph 0161, Paragraph 0163, and Paragraph 0169);
determining actual allocation data corresponding to the at least one energy source node of the one or more energy source nodes based on the real time power consumption data, the real time power generation data, the predicted power consumption data, and the predicted power generation data (See Paragraph 0159, Paragraph 0161 – “aggregating, by at least one processor, dynamic allocation values from a plurality of system nodes including at least the user input indicative of a first demand and the dynamic allocation flexibility, and generating, by at least one processor, a learning model for evaluating dynamic future allocation with future energy execution prediction, wherein the dynamic future allocation includes at least energy operational information based on a categorization of energy usage at a plurality of respective energy devices”, Paragraph 0163, and Paragraph 0169);
generating, based on the actual allocation data, a first control signal for the at least one energy source node associated with the identification data (See Paragraph 0159, Paragraph 0161, and Paragraph 0169 – “an energy device control system executing on a distributed grid subsystem operative to respond or control power demand for a plurality of appliances (e.g., energy devices (e.g., generators, consumers, etc.)), the energy device control is provided”);
and controlling, based on the first control signal, a first energy node controller, of a plurality of energy node controllers, connected to the at least one energy source node to distribute the energy amount from the at least one energy source node to the one or more energy sink nodes (See Paragraph 0159 – “trigger energy generation on the energy grid at respective generator nodes according to the learning model and dynamic projections”, Paragraph 0161, and Paragraph 0169 – “an energy device control system executing on a distributed grid subsystem operative to respond or control power demand for a plurality of appliances (e.g., energy devices (e.g., generators, consumers, etc.)), the energy device control is provided”).
Hannon does not specifically disclose capturing, “via one or more sensors associated with each of the one or more energy source nodes, real time power generation data” for each of one or more energy source nodes, the one or more energy source nodes are configured to generate energy; capturing, “via one or more sensors associated with each of the one or more energy sink nodes”, real time power consumption data. However, Matan et al. further teach:
capturing, “via one or more sensors associated with each of the one or more energy source nodes, real time power generation data” for each of one or more energy source nodes, the one or more energy source nodes are configured to generate energy (See Figure 1, Figure 3, Figure 12B, Paragraph 0110 – “one or more sensors, one or more grid-side controllers or data center, and local power demand and local conditions”, Paragraph 0143, Paragraph 0183 – “system 1700 generates capacity with one or more local energy sources 1760. Local energy source 1760 can be any type of energy generation system”, and Paragraph 0197 – “one or more sensors that monitor realtime data for the DER node, including … energy generation for one or more energy sources of the customer premises”);
capturing, “via one or more sensors associated with each of the one or more energy sink nodes”, real time power consumption data (See Figure 1, Figure 3, Figure 8 – “820”, Figure 12B, Paragraphs 0095-0096, Paragraph 0112, Paragraph 0197 – “one or more sensors that monitor realtime data for the DER node, including local demand information of loads for a customer premises of a power grid”, and claim 33 – “detecting the grid conditions of the power grid with a sensor of the customer premises”).
The teachings of Hannon and Matan et al. are related because both are analyzing energy use and consumption to make determinations about how to control energy resources. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the teachings of Hannon to incorporate the sensors of Matan et al. in order to ensure the most accurate data is being analyzed to make the determinations about energy device control.
Regarding Claim 3: Hannon in view of Matan et al. teach the limitations of claim 1. Hannon further teaches determining, at periodic intervals, the actual allocation data based on the real time power consumption data, the real time power generation data, the predicted power consumption data, and the predicted power generation data (See Paragraph 0159, Paragraph 0161 – “aggregating, by at least one processor, dynamic allocation values from a plurality of system nodes including at least the user input indicative of a first demand and the dynamic allocation flexibility, and generating, by at least one processor, a learning model for evaluating dynamic future allocation with future energy execution prediction, wherein the dynamic future allocation includes at least energy operational information based on a categorization of energy usage at a plurality of respective energy devices”, Paragraph 0163, and Paragraph 0169).
Regarding Claim 4: Hannon in view of Matan et al. teach the limitations of claim 1. Hannon further teaches: generating, based on the actual allocation data, a second control signal for at least one energy sink node of the one or more energy sink nodes; and controlling, based on the generated second control signal, a second energy node controller, of the plurality of energy node controllers, connected to the at least one energy sink node to draw an excess energy amount from the at least one energy sink node of the one or more energy sink nodes (See Paragraph 0085, Paragraph 0098 – “the energy management system is configured to incorporate the potential to inject energy during specific hours of the day into a battery and then withdraw during later hours of the day. In this way the larger operations of the grid may be normalized (e.g., inject at low consumption interval and return during high periods of consumption), and thus the system and the consuming customer are configured to more efficiently manage overall energy consumption without disruption or need for modification in existing architecture”, Paragraph 0159 – “trigger energy generation on the energy grid at respective generator nodes according to the learning model and dynamic projections”, Paragraph 0161 – “aggregating, by at least one processor, dynamic allocation values from a plurality of system nodes including at least the user input indicative of a first demand and the dynamic allocation flexibility, and generating, by at least one processor, a learning model for evaluating dynamic future allocation with future energy execution prediction, wherein the dynamic future allocation includes at least energy operational information based on a categorization of energy usage at a plurality of respective energy devices”, Paragraph 0163, and Paragraph 0169).
Regarding Claim 5: Hannon in view of Matan et al. teach the limitations of claim 4. Hannon further teaches wherein the actual allocation data comprises time data indicative of an optimal time to control at least one of: the energy amount to be drawn from the at least one energy source node or the excess energy amount to be drawn from the at least one energy sink node (See Paragraph 0085, Paragraph 0098 – “the energy management system is configured to incorporate the potential to inject energy during specific hours of the day into a battery and then withdraw during later hours of the day. In this way the larger operations of the grid may be normalized (e.g., inject at low consumption interval and return during high periods of consumption), and thus the system and the consuming customer are configured to more efficiently manage overall energy consumption without disruption or need for modification in existing architecture”, and Paragraph 0156).
Regarding Claim 6: Hannon in view of Matan et al. teach the limitations of claim 1. Hannon further teaches wherein the historic time series data include at least one of: historic power generation data, historic power consumption data, historic weather data, or historic conditional level consumption data (See Paragraph 0102 – “weather reports”, Paragraph 0127 – “Historical information about the generation and/or load valuations”, Paragraph 0159 – “generate a learning model for evaluating dynamic future allocation with future energy execution prediction”, Paragraph 0162 – “assigning categories of energy requirement for a respective device as part of generating the learning model for dynamic allocation flexibility. According to one embodiment, the method further comprising an act of selecting, by at least one processor, the category from a plurality of categories including at least a first category for non-deferrable energy consumption, a second category for a deferrable energy consumption having a time limited window for deferment, and a third category having a longer time window for deferment relative to the second category”, and Paragraphs 0163-0164).
Regarding Claim 7: Hannon in view of Matan et al. teach the limitations of claim 1. Hannon further teaches wherein the trained ML model corresponds to at least one of: a multivariate time series model, or a vector auto regression model (See Paragraph 0083 – “optimizing energy generation and delivery. In other examples, statistical analysis is used (including for example, regression modeling, and other approximation techniques)”, Paragraph 0159, Paragraph 0162, and Paragraphs 0163-0164).
Regarding Claim 8: Hannon in view of Matan et al. teach the limitations of claim 1. Hannon further teaches:
capturing, energy sink data corresponding to at least one energy sink node of the one or more energy sink nodes, the energy sink data include at least one of: energy sink type data, energy sink power consumption capacity data, energy sink location data, or conditional level consumption data (See Paragraph 0088 – “generation or load, and next-day or real-time (hourly) time period”, Paragraph 0090 – “load or generation, zonal data or individual nodes”, Paragraph 0100 – “location of the customer (e.g., more daylight in northern region during rather than in southern regions, etc.)”, and Paragraph 0153 – “generation/storage capacity”);
obtaining first predicted data corresponding to the at least one energy sink node of the one or more energy sink nodes, the first predicted data include at least one of: predicted first weather data, or predicted conditional level consumption data (See Paragraph 0086 – “model an hourly consumption estimate (load profile)”, Paragraph 0100 – “location of the customer (e.g., more daylight in northern region during rather than in southern regions, etc.)”, Paragraph 0102 – “weather reports”, Paragraph 0127 – “Historical information about the generation and/or load valuations”, and claim 1);
and determining using the trained ML model, the predicted power consumption data based on at least one of: the energy sink data, or the first predicted data (See Paragraph 0102 – “weather reports”, Paragraph 0127 – “Historical information about the generation and/or load valuations”, Paragraph 0159 – “generate a learning model for evaluating dynamic future allocation with future energy execution prediction”, Paragraph 0162 – “assigning categories of energy requirement for a respective device as part of generating the learning model for dynamic allocation flexibility. According to one embodiment, the method further comprising an act of selecting, by at least one processor, the category from a plurality of categories including at least a first category for non-deferrable energy consumption, a second category for a deferrable energy consumption having a time limited window for deferment, and a third category having a longer time window for deferment relative to the second category”, and Paragraphs 0163-0164).
Hannon does not specifically disclose capturing, via one or more sensors associated with each of the one or more energy sink nodes, energy sink data. However, Matan et al. further teach capturing, via one or more sensors associated with each of the one or more energy sink nodes, energy sink data (See Figure 1, Figure 3, Figure 8 – “820”, Figure 12B, Paragraphs 0095-0096, Paragraph 0112, Paragraph 0197 – “one or more sensors that monitor realtime data for the DER node, including local demand information of loads for a customer premises of a power grid”, and claim 33 – “detecting the grid conditions of the power grid with a sensor of the customer premises”).
The teachings of Hannon and Matan et al. are related because both are analyzing energy use and consumption to make determinations about how to control energy resources. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the teachings of Hannon to incorporate the sensors of Matan et al. in order to ensure the most accurate data is being analyzed to make the determinations about energy device control.
Regarding Claim 9: Hannon in view of Matan et al. teach the limitations of claim 6. Hannon further teaches:
capturing, energy source data corresponding to the at least one energy source node of the one or more energy source nodes, the energy source data include at least one of: energy source type data, energy source power generation capacity data, energy source installation data, or environmental impact data (See Paragraph 0100 – “location of the customer (e.g., more daylight in northern region during rather than in southern regions, etc.)”, Paragraph 0104 – “solar, wind, or other power generators”, Paragraph 0111 – “potential impact on overall grid health”, and Paragraph 0153 – “generation/storage capacity”);
obtaining, second predicted data corresponding to the at least one energy source node of the one or more energy source nodes, the second predicted data include at least predicted second weather data (See Paragraph 0088 – “generation or load, and next-day or real-time (hourly) time period”, Paragraph 0100 – “location of the customer (e.g., more daylight in northern region during rather than in southern regions, etc.)”, and Paragraph 0102 – “weather reports”);
and determining, using the trained ML model, the predicted power generation data based on at least one of: the energy source data or the second predicted data (See Paragraph 0102 – “weather reports”, Paragraph 0127 – “Historical information about the generation and/or load valuations”, Paragraph 0159 – “generate a learning model for evaluating dynamic future allocation with future energy execution prediction”, Paragraph 0162 – “assigning categories of energy requirement for a respective device as part of generating the learning model for dynamic allocation flexibility. According to one embodiment, the method further comprising an act of selecting, by at least one processor, the category from a plurality of categories including at least a first category for non-deferrable energy consumption, a second category for a deferrable energy consumption having a time limited window for deferment, and a third category having a longer time window for deferment relative to the second category”, and Paragraphs 0163-0164).
Hannon does not specifically disclose capturing, via one or more sensors associated with each of the one or more energy source nodes, energy source data. However, Matan et al. further teach capturing, via one or more sensors associated with each of the one or more energy source nodes, energy source data (See Figure 1, Figure 3, Figure 12B, Paragraph 0110 – “one or more sensors, one or more grid-side controllers or data center, and local power demand and local conditions”, Paragraph 0143, Paragraph 0183 – “system 1700 generates capacity with one or more local energy sources 1760. Local energy source 1760 can be any type of energy generation system”, and Paragraph 0197 – “one or more sensors that monitor realtime data for the DER node, including … energy generation for one or more energy sources of the customer premises”).
The teachings of Hannon and Matan et al. are related because both are analyzing energy use and consumption to make determinations about how to control energy resources. Therefore it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the teachings of Hannon to incorporate the sensors of Matan et al. in order to ensure the most accurate data is being analyzed to make the determinations about energy device control.
Regarding Claim 10: Hannon in view of Matan et al. teach the limitations of claim 1. Hannon further teaches determining, by using an optimization model, the actual allocation data by updating the predicted allocation data, wherein the predicted allocation data is updated, based on at least one of: the predicted power consumption data, the predicted power generation data, the real time power consumption data, or the real time power generation data corresponding to the at least one energy source node of the one or more energy source nodes (See Paragraph 0088, Paragraph 0131 – “actual hourly consumption profile”, Paragraph 0140 – “actual usage during a day”, Paragraph 0159, Paragraph 0161 – “aggregating, by at least one processor, dynamic allocation values from a plurality of system nodes including at least the user input indicative of a first demand and the dynamic allocation flexibility, and generating, by at least one processor, a learning model for evaluating dynamic future allocation with future energy execution prediction, wherein the dynamic future allocation includes at least energy operational information based on a categorization of energy usage at a plurality of respective energy devices”, Paragraph 0163, Paragraph 0167, and Paragraph 0169).
Regarding Claim 11: Hannon in view of Matan et al. teach the limitations of claim 10. Hannon further teaches wherein the optimization model corresponds to an unconstrained multi-variable optimization model (See Paragraph 0090 – “the rules are executed based on optimizing generation, load, and demand, coupled with forecasted need, price, and/or user categorizations of use” and the Examiner interprets the optimization being performed in Hannon uses many variables to optimize different features of the electrical grid system).
Regarding Claim 12: Hannon in view of Matan et al. teach the limitations of claim 1. Hannon further teaches determining the actual allocation data by applying a deterministic set of rules on at least one of: the predicted power consumption data, the predicted power generation data, the real time power consumption data, or the real time power generation data (See Paragraph 0088, Paragraph 0090 – “the rules are executed based on optimizing generation, load, and demand, coupled with forecasted need, price, and/or user categorizations of use”, Paragraph 0131 – “actual hourly consumption profile”, Paragraph 0140 – “actual usage during a day”, Paragraph 0159, Paragraph 0161 – “aggregating, by at least one processor, dynamic allocation values from a plurality of system nodes including at least the user input indicative of a first demand and the dynamic allocation flexibility, and generating, by at least one processor, a learning model for evaluating dynamic future allocation with future energy execution prediction, wherein the dynamic future allocation includes at least energy operational information based on a categorization of energy usage at a plurality of respective energy devices”, Paragraph 0163, Paragraph 0167, and Paragraph 0169).
Regarding Claim 13: Hannon in view of Matan et al. teach the limitations of claim 1. Hannon further teaches determining, at different times of day, an energy price corresponding to the energy amount to be drawn from the at least one energy source node associated with the identification data; and generating, based on the energy price and the real time power consumption data, an energy bill report to at least one energy consumer associated with at least one energy sink node of the one or more energy sink nodes, the energy bill report includes at least one of: a predetermined time period energy usage bill, or a total energy usage bill (See Paragraph 0112 – “the results can be nominated, confirmed, metered, and billed”, Paragraph 0115 – “on demand supply/pricing (e.g., 6 hour peaks vs hourly information)”, Paragraph 0118 – “real-time pricing”, Paragraph 0131 – “Prices are then displayed for the relevant time periods of Next Hour, Next Day weighted by the estimated (actual hourly consumption profile), and the Average of weighted Next Day prices for the previous 12 months”, and Paragraph 0133).
Regarding Claims 14-20: Claims 14-20 recite limitations already addressed by the rejections of claims 1-13 above; therefore the same rejections apply.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record, but not relied upon is considered pertinent to applicant's disclosure is listed on the attached PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625
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