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 .
Claim Rejections - 35 USC § 102
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 1-10, 12-14, 18 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by SANDERS et al. US 2017/0005515 A1.
SANDERS teaches:
1. An AI-based platform for enabling intelligent orchestration and management of power and energy, comprising:
a set of adaptive, autonomous data handling systems, [Fig. 30, server computers 30002, 30004, 30006]
wherein each of the adaptive, autonomous data handling systems is configured to
collect data relating to energy generation, storage, or delivery from a set of edge devices that are in operational control of a set of distributed energy resources and [para. 0411, “In other aspects of the energy cloud software platform, the platform may be configured wherein each one of the virtual devices of the set of virtual devices can report state parameters, operational history, errors, configuration parameters, and telemetry data to a server and wherein the data from each virtual device may be aggregated in a device report delivered to a remote cloud computing platform 11050.”]
is configured to autonomously adjust, based on the collected data, a set of operational parameters for such operational control. [Fig. 28B GUIDANCE, RULES, COMMANDS, sent to DERs 28052 ]
SANDERS teaches:
2. The AI-based platform of claim 1, wherein each of the adaptive, autonomous data handling systems is further configured to adapt a transport of data over a network and/or communication system, wherein the adapting is based on one or more 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. [Fig. 28B PRICES (market factor condition) 28058]
SANDERS teaches:
3. The AI-based platform of claim 1, wherein each of the adaptive, autonomous data handling systems includes an adaptive energy digital twin [Fig. 28B PREDICTIVE ANALYTICS 28042] that represents one or more 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, or an energy usage priority condition. [para. 0435, “In other certain aspects and continuing with aspects of the methods not shown but related to FIGS. 16A and 16B, the computer implemented method is provided wherein implementing one or more weather forecast routines associated with one or more predictive algorithms and one or more weather data sets stored in the energy management system or retrieved from an external application includes forming a data set of historical meteorological values with historical energy demand values at one or more user sites to model future energy demand of the one or more user sites to be placed on the utility grid and to use model future energy demand to forecast when the threshold energy demand of the one or more user sites will be exceeded 16080.” (emphasis added.)]
SANDERS teaches:
4. The AI-based platform of claim 1, wherein each of the adaptive, autonomous data handling systems includes an adaptive energy digital twin that is configured to perform one or more of, providing a visual and/or analytic indicator of energy consumption by one or more energy consumers, filtering energy data, highlighting energy data, or adjusting energy data. [Visualization 29078]
SANDERS teaches:
5. The AI-based platform of claim 1, wherein each of the adaptive, autonomous data handling systems includes an adaptive energy digital twin that is configured to generate a visual and/or analytic indicator of energy consumption by one or more of, one or more machines, one or more factories, or one or more vehicles in a vehicle fleet. [Figs. 6A and 6C dashboard indicators 605 and 635 for DERs]
SANDERS teaches:
6. The AI-based platform of claim 1, wherein each of the adaptive, autonomous data handling systems is further configured to perform one or more of, extracting energy-related data, detecting and/or correcting errors in energy-related data, transforming, converting, normalizing, and/or cleansing energy-related data, 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. [para. 0074, “The data from each virtual device can be aggregated in the device/status report, which can be delivered or requested periodically (e.g., every five minutes) from the site gateway platform.”]
SANDERS teaches:
7. The AI-based platform of claim 1, wherein the energy edge data is based on one or more public data resources, the public data resources including one or more 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. [para. 0047, “Through the intelligent processes provided by the various embodiments described herein, over time the system can learn about the specific features and characteristics of the site (e.g., weather patterns, load profiles, etc.) and can make adjustments on its own.”]
SANDERS teaches:
8. The AI-based platform of claim 1, wherein the energy edge data is based on one or more enterprise data resources, the enterprise data resources including one or more of, resource planning data, sales and/or marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data. [para. 0049, “Demand Charge Reduction. For commercial customers that are subject to demand charges, wherein costs are pegged to the maximum amount of power consumption on a monthly basis, the site resident system can monitor real-time demand and dispatch power to ensure the site load does not exceed the specified thresholds.”]
SANDERS teaches:
9. 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, and the training data set is based on one or more of, one or more human tags and/or labels, one or more human interactions with a hardware and/or software system, one or more outcomes, one or more AI-generated training data samples, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process. [para. 0132, “In one aspect, if the resident or business is on a time-of-use rate plan, the SIS and software platform will know how to minimize the cost of energy for that customer by charging when prices are low and dispatching when prices are high by machine learning adaptations of one or more rule sets comparing an expected output to an actual output, measuring the error, and making revisions to one or more rules to minimize the error by updating the expected output using in certain aspects an artificial intelligence component.”]
SANDERS teaches:
10. The AI-based platform of claim 1, wherein each of the adaptive, autonomous data handling systems is further configured to orchestrate delivery of energy to one or more points of consumption, and the delivery of the energy includes one or more of, one or more fixed transmission lines, one or more instances of wireless energy transmission, one or more deliveries of fuel, or one or more deliveries of stored energy. [para. 0133, “In other aspects, methods for voltage control allow the SIS site integration system/Distributed Energy Resource Energy Storage (DER-ES) Apparatus to respond to needs for voltage control by injecting or absorbing power at the place where it is needed most: nearest the load. Aggregated apparatus act as a fleet to provide orchestrated voltage optimization on a given circuit or feeder.”]
SANDERS teaches:
12. The AI-based platform of claim 1, wherein at least one of the adaptive, autonomous data handling systems is deployed in an off-grid environment, and the off-grid environment includes one or more of, an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system. [para. 0183, micro-grid control when grid is down]
SANDERS teaches:
13. The AI-based platform of claim 1, wherein the platform further comprises an adaptive energy data pipeline configured to communicate data across a set of nodes in a network. [Fig. 5B cloud based grid and energy cloud controller communicates across WAN to DERs]
SANDERS teaches:
14. The AI-based platform of claim 13, 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 edge networking devices. [Fig. 5B DERs 1-N 5002]
SANDERS teaches:
18. The AI-based platform of claim 13, wherein the adaptive energy data pipeline includes a self-organizing data storage, the data storage being configured to store data on a device based on one or more of patterns of the data, content of the data, or context of the data. [para. 0075, “The site management system may also be configured to access and use a data storage device either directly or via the interface.”]
SANDERS teaches:
20. The AI-based platform of claim 13, wherein the adaptive energy data pipeline is configured to perform enterprise contextual adaptation by automatically processing data based on one or more of an operating context of an enterprise, a transactional context of an enterprise, or a financial context of an enterprise. [para. 0153, time of use and prices paid used for peak management]
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.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over SANDERS et al. US 2017/0005515 A in view of Ghaemi et al. US 2019/0356164 A1.
SANDERS does not teach the following limitation, however, Ghaemi teaches:
11. The AI-based platform of claim 1, wherein each of the adaptive, autonomous data handling systems is further configured to record, in a distributed ledger and/or blockchain, one or more energy-related events, the one or more energy-related events including one or more 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. [Fig. 10 blockchain 1020 for DER controller 1050]
It would have been obvious to a person having ordinary skill in the art before the time of filing to combine the teachings of Ghaemi with those of SANDERS. A person having ordinary skill in the art would have been motivated to combine the teachings because Ghaemi teaches a distributed ledger (blockchain) can be used by DER controllers for solving optimization problems for global model predictive control (See para. 0032-0034).
Claims 15-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over SANDERS et al. US 2017/0005515 A in view of Forbes, JR. US 2014/0277788 A1.
SANDERS does not teach the following limitation, however, Forbes, JR teaches:
15. The AI-based platform of claim 13, 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 selection being based on a low-priority energy use related to the data. [para. 0099 and para. 0102, describe transmitting power grid data along with priority for QoS routing]
It would have been obvious to a person having ordinary skill in the art before the time of filing to combine the teachings of Forbes, JR with those of SANDERS. A person having ordinary skill in the art would have been motivated to combine the teachings because Forbes, JR teaches that priority based IP routing can be used for communications of grid related data including distributed energy resources (See para. 0099 and 0102).
FORBES, JR teaches:
16. The AI-based platform of claim 13, 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 selection being based on a high-priority energy use related to the data. [para. 0099 and para. 0102, describe transmitting power grid data along with priority for QoS routing]
FORBES, JR teaches:
17. The AI-based platform of claim 13, wherein the adaptive energy data pipeline includes a set of artificial intelligence capabilities, the capabilities being configured to adapt the pipeline to enable optimization of elements of data transmission in coordination with energy orchestration needs. [para. 0059, grid control IP routing with priority associated with each message]
FORBES, JR teaches:
19. The AI-based platform of claim 13, wherein the adaptive energy data pipeline is configured to perform automated, adaptive networking, the adaptive networking including one or more 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. [para. 0203]
Conclusion
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/GARY COLLINS/Examiner, Art Unit 2115