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 .
Information Disclosure Statement
IDS filed 10/29/2024 and 4/4/2024 are being considered by the examiner
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 limitation(s) is/are:
"a data processing system configured to fuse" in claim 1;
"an adaptive energy digital twin that is configured to perform" in claim 7;
"an adaptive energy digital twin that is configured to generate" in claim 8;
"a policy and governance engine that is configured to deploy" in claim 18; and
"an analytic system … configured to provide" in claim 19.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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 this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/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 limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites fusing at least one entity of an energy grid entity generation, storage, delivery or consumption grid data set with at least one entity of an off-grid energy entity generation, storage, delivery and/or consumption data set.
The limitations of fusing the energy grid entity data set with the off-grid energy entity data set is a process that under its broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting "a data processing system configured to fuse," nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the "data processing system configured to" language, "fuse" in the context of this claim encompasses the user manually combining or aggregating the data sets. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a data processing system to perform the fusing. The data processing system is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of combining) such that it amounts to no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a data processing system to perform the fusing amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claims 2-20 merely provide insignificant extra-solution activities that are incidental to the fusing and do not include additional elements that integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 6-9, 11, and 13-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hannon [US Pub. 2020/0106269].
With regard to claim 1, Hannon teaches an AI-based platform for enabling intelligent orchestration and management of power and energy ("a learning model for evaluating dynamic future allocation with future energy execution prediction [abstract]"), comprising:
a data processing system [fig. 4: energy management system (400)] configured to fuse
at least one entity of an energy grid entity generation, storage, delivery or consumption grid data set ("The indexing program 406 is configured to inspect incoming data from the ISOs via an inspection module [par. 0089]") with at least one entity of an off-grid energy entity generation, storage, delivery and/or consumption data set ("DG consists of some form of generation (solar panels, wind generator, fuel cell) which typically is located and operates behind the customer meter and may be (or may not) be combined with a battery … the system is configured to capture the parameters of these facilities, i.e., generation/storage capacity, ramp up time, production profiles, etc. and assign them to a category much like all other load bearing devices. The system can then filter these parameters through both the utility tariff analysis as well as any requirements specified by respective ISOs (e.g., via the ISO's DG (Net Meter) Protocol) [par. 0153]" and "The optimal load profile combines both purchased power from the Grid with both displaced power from the Grid from battery capacity, and also injects into the Grid surplus generation [par. 0155]").
Note: claim is presented in the alternative.
With regard to claim 2, Hannon teaches the AI-based platform of claim 1, wherein the data processing system is configured to automatically time align energy grid entity data with off-grid energy entity data ("state 3: same day purchase (e.g., hourly) based upon both realized and expected hourly prices from the ISO as well system based modeling (see. FIG. 30); and state 4: same day sale showing adjusted schedules, which can be based upon reduction in consumption or outright injection into the Grid from either Solar or Batteries, or various combinations of both options (see FIG. 31) [par. 0141]" and "The system then generates a schedule for energy request/delivery which is then nominated for execution. This optimization allows the system to account for scenarios where true hourly pricing is available [par. 0143]").
With regard to claim 3, Hannon teaches the AI-based platform of claim 1, wherein the data processing system is configured to automatically collect off-grid energy entity sensor data from a set of edge devices via which a set of off-grid energy entities are controlled ("retrieving, via an electronic interface, operation information associated with devices attached to the energy grid (e.g., energy consuming, energy generating, and/or hybrid devices), the operation information including data relating to usage, a location, and a value (e.g., fabrication value of electricity) for the associated devices, receiving, from a plurality of energy devices, requests for energy fabrication and requirements associated with those requests for energy loads input into a user interface connected to the energy device … scheduling, provision of energy from the energy grid to the plurality of energy devices, and communicating the fabrication information to the distribution nodes via one or more of the distributed grid controllers based on the learning model of energy consumption, and triggering delivery of energy in accordance with the scheduled provision of energy [par. 0163];" it is implied that a sensor is used to obtain operation information).
With regard to claim 4, Hannon teaches the AI-based platform of claim 1, wherein the data processing system is configured to automatically normalize the energy grid entity data and the off-grid energy entity data such as to present the data according to a set of common units ("predefined rules from a predefined rule set are programmatically applied to the retrieved operation information to translate it from its native format into a standardized format [par. 0075]" and "In such cases, offers to provide energy and preferences associated with those offers may be provided to the grid operator by end users who wish to either curtail their consumption or deliver energy they have produced and or stored (ex. solar panels and batteries). Scheduling of that provisioning may be carried out in a similar way, e.g., based on the received offers, preferences associated with the received offers, and standardized location data and standardized value data [par. 0078]" and "The native formats obtained also may be converted into this more standardized format in certain example embodiments, e.g., for aggregation, comparison, display, and/or other purposes [par. 0088]").
With regard to claim 6, Hannon teaches the AI-based platform of claim 1, further comprising an adaptive energy digital twin that represents at least one of,
an energy stakeholder entity,
an energy distribution resource,
a stakeholder information technology,
a networking infrastructure entity,
an energy-dependent stakeholder production facility,
a stakeholder transportation system,
a market condition ("the system is configured to compute multiple distinct tariffs based on different generation and load distribution scenarios, and present visualizations of the same in the user interface [par. 0133]"), or
an energy usage priority condition.
Note: claim is presented in the alternative.
With regard to claim 7, Hannon teaches the AI-based platform of claim 1, further comprising an adaptive energy digital twin that is configured to perform at least one of,
providing a visual and/or analytic indicator of energy consumption by at least one energy consumer,
filtering energy data,
highlighting energy data ("the system is configured to compute multiple distinct tariffs based on different generation and load distribution scenarios, and present visualizations of the same in the user interface [par. 0133]"), or
adjusting energy data.
Note: claim is presented in the alternative.
With regard to claim 8, Hannon teaches the AI-based platform of claim 1, further comprising an adaptive energy digital twin that is configured to generate a visual and/or analytic indicator of energy consumption by at least one of,
at least one machine ("retrieving, via an electronic interface, operation information associated with devices attached to the energy grid (e.g., energy consuming, energy generating, and/or hybrid devices), the operation information including data relating to usage, a location, and a value (e.g., fabrication value of electricity) for the associated devices, receiving, from a plurality of energy devices, requests for energy fabrication and requirements associated with those requests for energy loads input into a user interface connected to the energy device [par. 0163]"),
at least one factory, or
at least one vehicle in a vehicle fleet.
Note: claim is presented in the alternative.
With regard to claim 9, Hannon teaches the AI-based platform of claim 1, wherein the data processing system is further configured to perform at least one of,
extracting energy-related data,
detecting and/or correcting errors in energy-related data,
transforming, converting, normalizing, and/or cleansing energy-related data ("predefined rules from a predefined rule set are programmatically applied to the retrieved operation information to translate it from its native format into a standardized format [par. 0075]"),
parsing energy-related data,
detecting patterns, content, and/or objects in energy-related data,
compressing energy-related data,
streaming energy-related data,
filtering energy-related data,
loading and/or storing energy-related data,
routing and/or transporting energy-related data, or
maintaining security of energy-related data.
Note: claim is presented in the alternative.
With regard to claim 11, Hannon teaches the AI-based platform of claim 1, wherein the data processing system is further configured to orchestrate delivery of energy to at least one point of consumption ("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 [par. 0159]"), 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 ("the system can be configured to prevent any peak usage from exceeding this level through a withdrawal from battery capacity with any excess sold back into the Grid [par. 0156]").
Note: claim is presented in the alternative.
With regard to claim 13, Hannon teaches the AI-based platform of claim 1, wherein the at least one entity of an off-grid energy generation, storage, and/or consumption data set is deployed in an off-grid environment ("DG consists of some form of generation (solar panels, wind generator, fuel cell) which typically is located and operates behind the customer meter and may be (or may not) be combined with a battery [par. 0153]"), and the off-grid environment includes at least one of,
an off-grid energy generation system ("DG consists of some form of generation (solar panels, wind generator, fuel cell) which typically is located and operates behind the customer meter and may be (or may not) be combined with a battery [par. 0153]"),
an off-grid energy storage system, or
an off-grid energy mobilization system.
Note: claim is presented in the alternative.
With regard to claim 14, Hanno teaches the AI-based platform of claim 1, wherein the data processing system is further configured to intelligently orchestrate and manage power and/or energy based on a data set of energy generation, storage, and/or consumption data for a set of infrastructure assets ("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 [par. 0159]"), and
the data set is produced at least in part by a set of sensors contained in and/or governed by a set of edge devices ("retrieving, via an electronic interface, operation information associated with devices attached to the energy grid (e.g., energy consuming, energy generating, and/or hybrid devices), the operation information including data relating to usage, a location, and a value (e.g., fabrication value of electricity) for the associated devices, receiving, from a plurality of energy devices, requests for energy fabrication and requirements associated with those requests for energy loads input into a user interface connected to the energy device … scheduling, provision of energy from the energy grid to the plurality of energy devices, and communicating the fabrication information to the distribution nodes via one or more of the distributed grid controllers based on the learning model of energy consumption, and triggering delivery of energy in accordance with the scheduled provision of energy [par. 0163];" it is implied that a sensor is used to obtain operation information).
Note: claim is presented in the alternative.
With regard to claim 15, Hannon teaches the AI-based platform of claim 1, wherein the data processing system is further configured to manage at least one of,
generation of energy by a set of distributed energy generation resources,
storage of energy by a set of distributed energy storage resources,
delivery of energy by a set of distributed energy delivery resources, or
consumption of energy by a set of distributed energy consumption resources ("an energy device control system executing on a distributed grid subsystem operative to control a first power demand of a plurality of appliances (e.g., energy devices (e.g., generators, energy consumers, etc.)) [par. 0159]").
Note: claim is presented in the alternative.
With regard to claim 16, Hannon teaches the AI-based platform of claim 1, wherein the data processing system is further configured to intelligently orchestrate and manage power and/or energy of a set of entities ("demonstrating that aggregated data, including nodal data, can be visualized by the system to provide useful information in making power-related transactions interactive and subject to a smart grid, in accordance with certain example embodiments- and to provide visualization of the information the system analyses to execute various optimization [par. 0134]"),
wherein the set of entities includes at least one of,
a weather data resource,
a satellite data resource,
a census, population, demographic, and/or psychographic data resource,
a market data resource, or
an ecommerce data resource ("information about the loads, retail and wholesale prices, utility involved, substation involved [par. 0133]").
Note: claim is presented in the alternative.
With regard to claim 17, Hannon teaches the AI-based platform of claim 1, wherein the data processing system is further configured to execute at least one algorithm that perform a simulation of energy consumption by at least one of the entities, wherein the simulation is based on a data set that includes alternative state or event parameters for at least one of the entities that reflect alternative consumption scenarios, and the algorithms accesses a demand response model that accounts for how energy demand responds to changes in a price of energy or a price of an operation or activity for which the energy is consumed ("An estimated savings value also is presented to the user. Details concerning how this information was obtained may be provided. For instance, information about the loads, retail and wholesale prices, utility involved, substation involved, etc., may be shown on-screen and/or via drill-down options. As shown in the FIG. 23 example, the customer would pay just under $0/04 for energy under wholesale pricing, as compared to the marketer cost of $0.09 or more, thereby enabling the user to save over 60% by obtaining energy directly from the distribution point. According to one embodiment, the system is configured to compute multiple distinct tariffs based on different generation and load distribution scenarios, and present visualizations of the same in the user interface. In one example, the system provides visualizations to isolate peaking type charges by looking at the data, etc [par. 0133]" and "The system can be configured to perform this analysis on static load profiles, i.e., without preferences combined with market prices (and may also perform this analysis on load profiles with preference information). The result is compared to what actually can be realized (e.g. by applying filters on restrictions, operation requirements, etc.) in cost savings as a function of available tariffs or other restrictions specific to the customer, ISO, Utility, etc. In further embodiments, as the analysis by the system begins to incorporate demand response (DR), DG, or Net Metering the system defines an optimal solution, and then the available solution as constrained by the ISO's protocol which may reflected in the Utility's available tariffs for such activities [par. 0146]").
With regard to claim 18, Hannon teaches the AI-based platform of claim 1, wherein the data processing system includes a policy and governance engine that is configured to deploy a set of rules and/or policies to at least one edge device that is in local communication with at least one of the entities, and the edge device is configured to govern at least one of the entities based on the rules and/or policies ("an energy device control system executing on a distributed grid subsystem operative to control a first power demand of a plurality of appliances (e.g., energy devices (e.g., generators, energy consumers, etc.)), is provided. The system comprises a graphical user interface configured to accept a user input indicative of a first demand and dynamic allocation flexibility associated with the a respective energy device, a communication interface configured to aggregate 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 at least one processor programmed to 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 [par. 0159]").
Note: claim is presented in the alternative.
With regard to claim 19, Hannon teaches the AI-based platform of claim 1, wherein the data processing system includes an analytic system that represents a set of operating parameters and current states of at least one of
the entities based on a set of sensed parameters ("retrieving, via an electronic interface, operation information associated with devices attached to the energy grid (e.g., energy consuming, energy generating, and/or hybrid devices), the operation information including data relating to usage, a location, and a value (e.g., fabrication value of electricity) for the associated devices, receiving, from a plurality of energy devices, requests for energy fabrication and requirements associated with those requests for energy loads input into a user interface connected to the energy device … scheduling, provision of energy from the energy grid to the plurality of energy devices, and communicating the fabrication information to the distribution nodes via one or more of the distributed grid controllers based on the learning model of energy consumption, and triggering delivery of energy in accordance with the scheduled provision of energy [par. 0163]"),
the set of sensed parameters is generated by a set of edge devices that are in proximity to at least one of the entities, and
the analytic system is configured to provide a recommendation associated with at least one the at least one of the entities or at least one additional available entity.
Note: claim is presented in the alternative.
With regard to claim 20, Hannon teaches the AI-based platform of claim 1, wherein the data processing system includes an artificial intelligence system that is trained on a historical data set relating to
energy generation, storage, and/or utilization of an operating process associated with at least one of the entities ("Although such historical information may be fed into models that take into account temperature and/or other environmental factors to help refine estimates, these models are essentially static and backward looking, not taking into account how customer's consumption patterns may change given alternative price scenarios [par. 0010]" and "When combined with the historic hourly usage by the customer and their preferences the system is able to generate a result which is the 'optimal load profile' by hour and also a schedule which specifies energy bids 'up to' the price which is expected to be realized for that hour [par. 0147]"), and
the data processing system is further configured to, analyze an energy pattern for the operating process, and output a forecast of energy requirements of the operating process based on a current state and/or information associated with at least one of the entities ("the system is configured to analyze historic hourly prices for this location and time period, as well as results from next day nominations (e.g., determination made by the system), and can also include analysis of current weather, economic activity etc., to create a system based market price, that enables further optimization on whether an end user should modify its consumption behavior for the hour to come [par. 0148]" and " the system can include an analytics engine configured to match end user to wholesale price and tariff, capture historic consumption information, translate consumption (generation) into device level usage, assign priorities based upon customer preferences, create optimal load and achievable load profiles, factor in historic wholesale hourly pricing, external factors, e.g., weather, grid stress, etc. and then submit a next day load allocation request. Wherein the system process this order through all the required states; nomination, confirmation, metering, settlement, invoicing, accounting for location specific constraints [par. 0157]").
Note: claim is presented in the alternative.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Hannon in view of Lin et al. [US Pub. 2016/0006667].
With regard to claim 5, Hannon teaches the AI-based platform of claim 1.
Although Hannon teaches wherein the data processing system is further configured to transport data over a network and/or communication system ("a communication interface configured to aggregate dynamic allocation values from a plurality of system nodes [par. 0159]"),
Hannon does not explicitly teach wherein the adapting is based on at least one of, a congestion condition, a delay and/or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality-of-service (QoS) condition, a usage condition, a market factor condition, or a user configuration condition.
In the same field of endeavor (network communication), Lin teaches to adapt a transport of data, wherein the adapting is based on at least one of,
a congestion condition,
a delay and/or latency condition,
a packet loss condition ("performs self-adaptive processing on packet loss or congestion occurring in a network during real-time communication, i.e., adjusts the code rate and transmission position of protected data in real time according to network conditions, thereby effectively preventing voice discontinuousness, image mess or pause occurring at a client, and improving the user experience [abstract]"),
an error rate condition,
a cost of transport condition,
a quality-of-service (QoS) condition,
a usage condition,
a market factor condition, or
a user configuration condition.
It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have included Lin's concept of adapting processing based on packet loss, with the teachings of Hannon, for the benefit of preventing issues due to network conditions.
Note: claim is presented in the alternative.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Hannon in view of Farrokhabadi et al. [US Pub. 2019/0362445] ("Farrokhabadi").
With regard to claim 10, Hannon teaches the AI-based platform of claim 1, further comprising 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 ("Although such historical information may be fed into models that take into account temperature and/or other environmental factors to help refine estimates, these models are essentially static and backward looking, not taking into account how customer's consumption patterns may change given alternative price scenarios [par. 0010]" and "When combined with the historic hourly usage by the customer and their preferences the system is able to generate a result which is the 'optimal load profile' by hour and also a schedule which specifies energy bids 'up to' the price which is expected to be realized for that hour [par. 0147]"),
Hannon does not explicitly teach where the training data set is based on at least one of, at least one human tag and/or label, at least one human interaction with a hardware and/or 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.
In an analogous art (model training), Farrokhabadi teaches where a training data set is based on at least one of,
at least one human tag and/or label,
at least one human interaction with a hardware and/or software system,
at least one outcome,
at least one AI-generated training data sample,
a supervised learning training process ("The training step can be performed using any number of training algorithms or machine learning training procedures including supervised learning, e.g., linear regression, or random forest algorithms [par. 0053]"),
a semi-supervised learning training process, or
a deep learning training process.
It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have included Farrokhabadi's known teachings of supervised learning, into the model taught by Hannon, since the combination predictably uses prior art elements according to their established functions to yield the predictable result of being able to train the model.
Note: claim is presented in the alternative.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Hannon in view of Arunachalam et al. [US Pub. 2023/0123959] ("Arunachalam").
With regard to claim 12, Hannon teaches the AI-based platform of claim 1. Hannon does not explicitly teach wherein the data processing system is further configured to 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 and/or sale event, a service charge associated with an energy purchase and/or 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.
In an analogous art (energy management), Arunachalam teaches wherein a data processing system is further configured to 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 and/or sale event ("The energy trading platform may further be configured to: receive notifications of available energy from one or more energy suppliers; receive requests for energy from one or more energy consumers; receive a selection from a first energy consumer of one or more of the energy suppliers to fulfill a request for energy of the first energy consumer; create a blockchain transaction in the blockchain distributed ledger between the first energy consumer and the one or more of the energy suppliers selected by the first energy consumer to fulfill the request for energy of the first energy consumer [par. 0006]"),
a service charge associated with an energy purchase and/or 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.
Arunachalam further teaches, "[i]n this way, the blockchain 600 may provide a tamper-evident distributed ledger that can be used by multiple nodes. This may help establish trust between the various nodes in the blockchain network without relying on or in addition to third-party digital certificates or self-signed digital certificates [par. 0075]."
It would have been obvious to one having ordinary skill in the art at the time of filing the invention to have included Arunachalam's teachings of a ledger, with the teachings of Hannon, for the benefit of establishing trust between various nodes of a power network.
Note: claim is presented in the alternative.
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Srinivasan et al. [U.S. Pub. 2022/0302712] teaches where systems and methods provide the ability for coordinated, real-time, and pre-planned control of a network of distributed energy resource systems where each distributed energy resource system is located at a different consumer site. Each distributed energy resource system is operable to provide stored and/or generated energy to meet on-site consumer needs and/or provide energy to the grid. An off-site control system obtains energy usage information from a plurality of the distributed energy resource systems and processes the information in order to coordinate control of each distributed energy resource system such as coordinating if and when each distributed energy resource system provides energy to the grid in order to at least partially meet energy demands associated with a demand response timeframe.
Conclusion
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/VINCENT WEN-LIANG CHANG/
Examiner
Art Unit 2119
/MOHAMMAD ALI/Supervisory Patent Examiner, Art Unit 2119