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 .
DETAILED ACTION
2. This action is in response to the Amendment filed December 8, 2023.
3. Claims 1-20 have been examined and are pending with this action.
4. The Information Disclosure Statements filed April 5, 2024, October 29, 2024, and January 28, 2026 have been considered.
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.
5. Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Hummon et al. (US 2024/0213800 A1).
INDEPENDENT:
As per claim 1, Hummon teaches an AI-based platform for enabling intelligent orchestration and management of power and energy (see Hummon, [0005]: “This technology can include or utilize a distributed internet-of-things (IOT) computing platform capable of at least one of deploying or updating machine learning (ML) models at the grid edge to allow a decentralized, bidirectional, DER-integrated utility grid.”), comprising:
a set of adaptive, autonomous data handling systems, wherein each of the adaptive, autonomous data handling systems is configured to collect data relating to at least one of energy generation, energy storage, energy delivery, or energy consumption from a set of artificial-intelligence enabled edge devices that are in operational control of a set of distributed energy resources and a set of legacy grid-based energy resources (see Hummon, [0006]: “Each metering device can capture or obtain data in relatively real-time. The metering device can include a graphic processing unit (GPU) configured to process collected data, thereby combining metrology and GPU processing capability to enable distributed edge computing or learning… For example, the SGDOP can combine the capabilities for utility usage of IoT that leverages the metering device (or other edge devices) as the edge compute-surface (e.g., surface computer) and augment the metering device capabilities with GPU capabilities to enable distributed artificial intelligence (AI) for at least managing various components within the utility grid, including but not limited to the DERs within the utility grid, such as PV, ESS, EVs (e.g., charging infrastructure or power distributor), or other electric generators or electric storage, providing visualization of the electrical condition (or other information related to the utility grid) at the grid edge, or providing the capabilities to support or integrate one or more applications from various (e.g., third-party) application developers, such as to the one or more edge devices. Hence, the deployment of the SGDOP can improve at least load management and DER value adjustment for the decentralized, bidirectional, DER-integrated utility grid.”; and [0034]: “The metering device can include a graphic processing unit (GPU) configured to process collected data, thereby combining metrology and GPU processing capability to enable distributed edge computing or learning. The SGDOP can include the IoT (e.g., open-IoT) platform, enabling the deployment and management of software applications for the metering devices. For instance, the SGDOP can combine the capabilities for utility usage of IoT that leverages the metering device as the edge compute-surface (e.g., surface computer) and augment the metering device capabilities with GPU capabilities to enable distributed artificial intelligence (AI) for managing various components within the utility grid. Hence, the deployment of the SGDOP can improve at least load management and DER value adjustment for the decentralized, bidirectional, DER-integrated utility grid.”); and
an adaptive energy data pipeline configured to receive collected data from the adaptive, autonomous data handling systems and communicate the received data across a set of nodes in a network (see Hummon, [0062]: “The secure cloud data services and API can allow (or enable) utility or third-party network (e.g., web) data aggregations or analytics across the SGDOP, including data and intelligence sharing across different tenants (e.g., edge devices or metering devices 118).”; and [0121]: “In various configurations, the runtime environment 214 can facilitate collaborative functionalities between the plurality of applications on the SGDOP, such as facilitating the communication or sharing of information between applications from the application developer device 242 (or other third-party application developers), preinstalled applications on the edge devices 201, etc. The applications may be executed independently on edge devices 201 or simultaneously between multiple edge devices 201. The runtime environment 214 can allow for data pathways such that applications can share or communicate data with each other. The data pathway, given the permissions or security criteria between the applications, can allow for the collaborative functionalities.”);
wherein the set of adaptive, autonomous data handling systems is configured to determine, based on the collected data, a mix of at least one of energy generation, energy storage, energy delivery, or energy consumption characteristics for a set of energy resources that are in local communication with the set of artificial-intelligence enabled edge devices and to output a data set that represents a constituent proportion of the mix (see Hummon, [0006]: “Each metering device can capture or obtain data in relatively real-time. The metering device can include a graphic processing unit (GPU) configured to process collected data, thereby combining metrology and GPU processing capability to enable distributed edge computing or learning.”; [0034]: “The metering device can include a graphic processing unit (GPU) configured to process collected data, thereby combining metrology and GPU processing capability to enable distributed edge computing or learning. The SGDOP can include the IoT (e.g., open-IoT) platform, enabling the deployment and management of software applications for the metering devices. For instance, the SGDOP can combine the capabilities for utility usage of IoT that leverages the metering device as the edge compute-surface (e.g., surface computer) and augment the metering device capabilities with GPU capabilities to enable distributed artificial intelligence (AI) for managing various components within the utility grid. Hence, the deployment of the SGDOP can improve at least load management and DER value adjustment for the decentralized, bidirectional, DER-integrated utility grid.”; [0089]: “For purposes of providing examples, the desired or predefined function for the edge device 201 can be for managing the delivery of electricity via the utility grid 100, although the application manager 206 can similarly identify the application(s) for other types of functions.”; and [0103]: “Using the application to manage the delivery of electricity as an example, the model can perform a prediction of changes in electrical demands at the metering device 118 associated with the edge device 201 over time. According to the output from the model, the application may manage the delivery of electricity by sending signals to one or more components of the utility grid 100 to adjust the electricity generation or distribution to the metering device 118 according to the changes in the electrical demands, for example.”).
As per claim 19, Hummon teaches a method of enabling intelligent orchestration and management of power and energy via an AI-based platform, comprising:
collecting, by a set of adaptive, autonomous data handling systems, data relating to at least one of energy generation, energy storage, energy delivery, or energy consumption from a set of artificial-intelligence enabled edge devices that are in operational control of a set of distributed energy resources and a set of legacy grid-based energy resources (see Claim 1 rejection above); and
receiving, by an adaptive energy data pipeline, collected data from the set of adaptive, autonomous data handling systems and communicate the received data across a set of nodes in a network (see Claim 1 rejection above);
wherein the set of adaptive, autonomous data handling systems is configured to determine, based on the collected data, a mix of at least one of energy generation, energy storage, energy delivery, or energy consumption characteristics for a set of energy resources that are in local communication with the set of artificial-intelligence enabled edge devices and to output a data set that represents a constituent proportion of the mix (see Claim 1 rejection above).
DEPENDENT:
As per claims 2 and 20, which respectively depend on claims 1 and 19, Hummon further teaches wherein AI-based platform further comprises an artificial intelligence system configured to orchestrate and manage delivery of energy to points of consumption based on the data set that represents a constituent proportion of the mix (see Hummon, [0004]: “To manage the changing load characteristics of these electrical distribution systems, the technical solution can provide at least one edge device (e.g., or electric consumption entities at the grid edge) capable of handling data processing operations (e.g., grid edge intelligence) at the edge of the utility distribution grid to optimize the configurations or decisions (e.g., adjustment of electrical input or output) for the DERs in real-time, allow for intelligent visualization of the electrical conditions at the grid edge (e.g., capabilities for acquiring the statuses or event information associated with the utility distribution grid and presenting the information to the users), and provide capabilities or supports for integrating one or more applications, such as third-party applications, from various application developers to one or more edge devices, among other non-limiting features or functionalities.”; [0049]: “One or more components, assets, or devices of utility grid 100 can communicate via network 140. The utility grid 100 can use one or more networks, such as public or private networks. The utility grid 100 can communicate or interface with a data processing system 150 designed and constructed to communicate, interface or control the utility grid 100 via network 140. Each asset, device, or component of utility grid 100 can include one or more computing devices 500 or a portion of computing device 500 or some or all functionality of computing device 500.”; [0059]: “In another example, as described in conjunction with at least FIG. 2, the data processing system 150 can include one or more features of the SGDOP to execute actions or send commands to components of the utility grid 100 to control or manage electricity generated or provided to metering devices 118 or edge devices.”; and [0103]: “According to the output from the model, the application may manage the delivery of electricity by sending signals to one or more components of the utility grid 100 to adjust the electricity generation or distribution to the metering device 118 according to the changes in the electrical demands, for example.”).
As per claim 3, which depends on claim 1, Hummon further teaches wherein the adaptive energy data pipeline is further configured to adapt a transport of data over at least one of a network or a communication system, wherein the adapting is based on at least one of, a congestion condition, a delay condition, a 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, and a user configuration condition (see Hummon, [0004]: “To manage the changing load characteristics of these electrical distribution systems, the technical solution can provide at least one edge device (e.g., or electric consumption entities at the grid edge) capable of handling data processing operations (e.g., grid edge intelligence) at the edge of the utility distribution grid to optimize the configurations or decisions (e.g., adjustment of electrical input or output) for the DERs in real-time, allow for intelligent visualization of the electrical conditions at the grid edge (e.g., capabilities for acquiring the statuses or event information associated with the utility distribution grid and presenting the information to the users), and provide capabilities or supports for integrating one or more applications, such as third-party applications, from various application developers to one or more edge devices, among other non-limiting features or functionalities.”; and [0044]: “Monitoring devices 118a-118n can be coupled through communications media 122a-122n to voltage controller 108. Voltage controller 108 can compute (e.g., discrete-time, continuously or based on a time interval or responsive to a condition/event) values for electricity that facilitates regulating or controlling electricity supplied or provided via the utility grid”).
As per claim 4, which depends on claim 1, Hummon further teaches wherein each of the adaptive, autonomous data handling systems is further configured to adapt a transport of data over at least one of a network or a communication system, wherein the adapting is based on at least one of, a congestion condition, a delay condition, a 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 (see Hummon, [0083]: “The data collector 204 can obtain network connection data. The network connection data can include at least one of but is not limited to source and destination endpoints or addresses, communication protocols, timestamps, data payload, quality of service metrics, or security measures.”; [0098]: “The model can include or correspond to a convolutional neural network (CNN) model, long-short-term-memory (LSTM) model, support vector machine (SVM), or other types of models. In some cases, the model manager 208 may use a combination of different types of models. The model manager 208 can receive training data for training the model. The model manager 208 may receive a model generated or trained by other devices within the network 140.”; and [0107]: “The list of models can include information associated with each model, such as version, update date, features or functionalities, training engine used for the model, etc.”).
As per claim 5, which depends on claim 1, Hummon teaches 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, 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 Hummon, [0082]: “The data collector 204 can obtain data from the edge device 201 including but not limited to hardware or software data associated with the edge device 201, such as installed hardware components (for hardware support), software or firmware version, applications installed, configurations, time since last updated, data security policy, location of the edge device 201, device identifier, or other information related to the edge device 201.”; [0106]: “In some cases, the model manager 208 may generate or train various models to configure for various functions of one or more applications. Each application may include one or more functions. For applications with more than one predefined type of function, the model manager 208 can configure multiple models for the application.”; and [0169]: “The data stream can include detected or determined grid events (e.g., electricity demands, faults, outages, or other events related to the delivery of electricity) at the at least one time interval. The data processing system can update or (re-)train the model based on the correlation between the electrical information and the grid events, e.g., to optimize the model for detecting or predicting grid events based on real-time information collected at the grid edge by one or more edge devices.”).
As per claim 6, which depends on claim 1, Hummon further teaches wherein at least one node of the set of nodes is 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 Hummon, [0004]: “The utility distribution grids (e.g., electrical distribution systems) can include distributed energy resources (DERs), such as photovoltaics (PV), energy storage systems (ESS), electric vehicles (EVs), and other electric generators or electric storage.”).
As per claim 7, which depends on claim 1, Hummon further teaches wherein at least one node of the set of nodes 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 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 Hummon, [0148]: “The V2G service can manage the dispatch or distribution of electrical power from at least one EV's battery (or other generators or electrical storage devices connected to the utility grid 100) into the utility grid 100. The dispatch resolution service can receive information regarding detected fault, outages, or other events in the utility grid. The dispatch resolution service can inform the utility operator of the event and recommended actions to resolve the detected event. The eContracts can involve using blockchain to process contracts (e.g., regulatory contracts) sent to or received by the edge device 201. The eContracts can allow virtual power plants and demand-side aggregators access to behind-the-meter devices (e.g., DERs, controllable loads) and bid the devices into wholesale or local markets. This structure can maintain data local and private, and eliminate conflicts between devices, aggregators, and the grid, for example.”).
As per claim 8, which depends on claim 1, Hummon further teaches wherein at least one node of the set of nodes is deployed in 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 Hummon, [0004]: “The utility distribution grids (e.g., electrical distribution systems) can include distributed energy resources (DERs), such as photovoltaics (PV), energy storage systems (ESS), electric vehicles (EVs), and other electric generators or electric storage.”).
As per claim 9, which depends on claim 1, Hummon further teaches wherein the adaptive energy data pipeline is further configured to monitor at least one of, an overall energy consumption by at least a portion of the set of nodes, or a role of at least one node of the set of nodes in an overall energy consumption by at least a portion of the set of nodes, and based on the monitoring, perform at least one of, managing an energy consumption by the set of nodes, forecasting an energy consumption by the set of nodes, or provisioning resources associated with energy consumption by the set of nodes (see Hummon, [0035]: “The utility grid 100 can include monitoring devices 118a-118n that can be coupled through optional potential transformers 120a-120n to secondary utilization circuits 116. The monitoring or metering devices 118a-118n can detect (e.g., continuously, periodically, based on a time interval, responsive to an event or trigger) measurements and continuous voltage signals of electricity supplied to one or more electrical devices 119 connected to circuit 112 or 116 from a power source 101 coupled to bus 102. These metering devices 118a-118n, among other components within utility distribution grids, can collect samples of power delivery or consumption, such as voltage information, at a predetermined sample rate. A voltage controller 108 can receive, via a communication media 122, measurements obtained by the metering devices 118a-118n, and use the measurements to make a determination regarding a voltage tap settings, and provide an indication to regulator interface 110. The regulator interface can communicate with voltage regulating transformer 106a to adjust an output tap level 106b.”).
As per claim 10, which depends on claim 1, Hummon further teaches wherein the set of nodes in the network that comprise the adaptive energy data pipeline comprise a set of edge networking devices that govern at least one of energy consumption, energy storage, energy delivery, or energy consumption by a set of operating devices that are controlled via the set of edge networking devices (see Hummon, [0004]: “These DERs can assist with the distribution of electricity, thereby allowing a decentralized, bidirectional smart grid. For decentralized, bidirectional smart utility grids, it can be challenging to manage the changing load characteristics of the enhanced power ecosystem using load prediction, DER balancing, or data collection of certain systems. To manage the changing load characteristics of these electrical distribution systems, the technical solution can provide at least one edge device (e.g., or electric consumption entities at the grid edge) capable of handling data processing operations (e.g., grid edge intelligence) at the edge of the utility distribution grid to optimize the configurations or decisions (e.g., adjustment of electrical input or output) for the DERs in real-time, allow for intelligent visualization of the electrical conditions at the grid edge (e.g., capabilities for acquiring the statuses or event information associated with the utility distribution grid and presenting the information to the users), and provide capabilities or supports for integrating one or more applications, such as third-party applications, from various application developers to one or more edge devices, among other non-limiting features or functionalities.”; and [0035]: “The utility grid 100 can include monitoring devices 118a-118n that can be coupled through optional potential transformers 120a-120n to secondary utilization circuits 116. The monitoring or metering devices 118a-118n can detect (e.g., continuously, periodically, based on a time interval, responsive to an event or trigger) measurements and continuous voltage signals of electricity supplied to one or more electrical devices 119 connected to circuit 112 or 116 from a power source 101 coupled to bus 102. These metering devices 118a-118n, among other components within utility distribution grids, can collect samples of power delivery or consumption, such as voltage information, at a predetermined sample rate. A voltage controller 108 can receive, via a communication media 122, measurements obtained by the metering devices 118a-118n, and use the measurements to make a determination regarding a voltage tap settings, and provide an indication to regulator interface 110. The regulator interface can communicate with voltage regulating transformer 106a to adjust an output tap level 106b.”).
As per claim 11, which depends on claim 1, Hummon further teaches wherein the adaptive energy data pipeline is further configured to automatically select a least-cost route for data communicated across the set of nodes, the selecting being based on a low-priority energy use related to the data (see Hummon, [0148]: “The dispatch resolution service can include one or more services, not limited to optimal power flow (OPF) service, vehicle to grid (V2G) service, or other services for the utility grid 100. The OPF service can provide calculations of the state variables of each element of the system (e.g., system 200) or the utility grid 100 to minimize losses while serving all loads at a minimal cost (e.g., optimizing resources for various edge devices 201 or loads).”).
As per claim 12, which depends on claim 1, Hummon further teaches wherein the adaptive energy data pipeline is further configured to automatically select a high-quality of service route for data communicated across the set of nodes, the selecting being based on a high-priority energy use related to the data (see Hummon, [0083]: “The data collector 204 can obtain network connection data. The network connection data can include at least one of but is not limited to source and destination endpoints or addresses, communication protocols, timestamps, data payload, quality of service metrics, or security measures.”; and [0119]: “The runtime environment 214 can provide a runtime environment on the data processing system 150 to host one or more applications configured to interface with the application executed or installed on the edge devices 201 within the system 200 (e.g., SGDOP). As part of the SGDOP architecture, for example, the runtime environment 214 can provide a runtime environment serving as an ecosystem that accommodates various applications to optimize grid operations, allow edge devices 201 to install applications from third-party application developers, or facilitate collaborative functionalities between edge devices 201 and the data processing system 150.”).
As per claim 13, which depends on claim 1, Hummon further teaches wherein the adaptive energy data pipeline includes a set of artificial intelligence capabilities, and the set of artificial intelligence capabilities is configured to adapt the pipeline to enable regulation of elements of data transmission in coordination with energy orchestration needs (see Hummon, [0004]: “To manage the changing load characteristics of these electrical distribution systems, the technical solution can provide at least one edge device (e.g., or electric consumption entities at the grid edge) capable of handling data processing operations (e.g., grid edge intelligence) at the edge of the utility distribution grid to optimize the configurations or decisions (e.g., adjustment of electrical input or output) for the DERs in real-time, allow for intelligent visualization of the electrical conditions at the grid edge (e.g., capabilities for acquiring the statuses or event information associated with the utility distribution grid and presenting the information to the users), and provide capabilities or supports for integrating one or more applications, such as third-party applications, from various application developers to one or more edge devices, among other non-limiting features or functionalities.”).
As per claim 14, which depends on claim 1, Hummon further teaches wherein the adaptive energy data pipeline includes a self-organizing data storage, and the self-organizing data storage is configured to store data on a device based on at least one of a pattern of the data, content of the data, or context of the data (see Hummon, [0146]: “Training the model on the cloud can allow the model to be deployed on the edge device 201 at a relatively faster pace because the model can tune itself to the edge device 201 without performing the training at the edge.”).
As per claim 15, which depends on claim 1, Hummon further teaches wherein the adaptive energy data pipeline is configured to perform automated, adaptive networking, the automated, adaptive networking including at least one of adaptive protocol selection, adaptive routing of data based on RF conditions, adaptive filtering of data, adaptive slicing of network bandwidth, adaptive use of cognitive network capacity, or adaptive use of peer-to-peer network capacity (see Hummon, [0048]: “The network 140 can include mobile telephone or data networks using any protocol or protocols to communicate among vehicles or other devices, including advanced mobile protocols, time or code division multiple access protocols, global system for mobile communication protocols, general packet radio services protocols, or universal mobile telecommunication system protocols, and the same types of data can be transmitted via different protocols.”; and [0081]: “The electrical data may include data processed by the metering device 118, such as filtered data, harmonic data, root-mean-square (RMS) value, compressed data, etc. The electrical data may be electrical waveform data (e.g., voltage waveform data or current waveform data) corresponding to electricity consumed by at least one load within the utility grid 100 and measured by at least one of the metering devices 118.”).
As per claim 16, which depends on claim 1, Hummon further teaches wherein the adaptive energy data pipeline is configured to perform enterprise contextual adaptation by automatically processing data based on at least one of an operating context of an enterprise, a transactional context of an enterprise, or a financial context of an enterprise (see Hummon, [0004]: “To manage the changing load characteristics of these electrical distribution systems, the technical solution can provide at least one edge device (e.g., or electric consumption entities at the grid edge)… ”; [0148]: “The eContracts can involve using blockchain to process contracts (e.g., regulatory contracts) sent to or received by the edge device 201. The eContracts can allow virtual power plants and demand-side aggregators access to behind-the-meter devices (e.g., DERs, controllable loads) and bid the devices into wholesale or local markets. This structure can maintain data local and private, and eliminate conflicts between devices, aggregators, and the grid, for example.”; and [0152]: “The DER dispatch can use stochastic optimization to determine optimal actions for DERs to take, given at least one of but not limited to price signals, distribution system operator (DSO) signals, user preferences, or power flow constraints. The DER dispatch can allow the utilities to satisfy predefined criteria or obligations according to a specification.”).
As per claim 17, which depends on claim 1, Hummon further teaches wherein at least one node of the set of nodes is further configured to adjust communication with at least one other node of the set of nodes to adapt a reporting, to the at least one other node, of data associated with at least one of energy generation, energy storage, energy delivery, or energy consumption (see Hummon, [0015]: “The application can determine a distance to a fault responsive to detection of a power outage notification. The data processing system can receive the application from a third-party application developer device remote from the data processing system.”; [0062]: “The secure cloud data services and API can allow (or enable) utility or third-party network (e.g., web) data aggregations or analytics across the SGDOP, including data and intelligence sharing across different tenants (e.g., edge devices or metering devices 118).”; and [0069]: “The edge device 201 can monitor electrical utilization or other grid information (e.g., in real-time) via the application, manage the application(s) installed on the metering device 118, or provide information to the user of the edge device 201 (e.g., via display device configured to display a graphical user interface (GUI), notification, or other types of interface).”).
As per claim 18, which depends on claim 1, Hummon further teaches wherein at least one node of the set of nodes is further configured to adapt reported data to at least one other node of the set of nodes, wherein adapting the reported data is based on a priority of a consumption of the reported data (see Hummon, [0153]: “The DER communication can ensure that power flow constraints from the utility are prioritized. The DER communication can ensure that DER controls are optimized based on these constraints. Multiple protocols can be supported, including heartbeat functionality, e.g., to alert devices to disconnection from the central system”).
Conclusion
7. For the reasons above, claims 1-20 have been rejected and remain pending.
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL Y WON whose telephone number is (571)272-3993. The examiner can normally be reached on Wk.1: M-F: 8-5 PST & Wk.2: M-Th: 8-7 PST.
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/Michael Won/Primary Examiner, Art Unit 2443