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 .
Response to Arguments
Applicant’s arguments, see Remarks, filed 9/11/2025, with respect to the rejection(s) of the claim(s) under 35 U.S.C. 102 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Guo CN-113132232-A.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 7-20 and 22-26 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Guo CN-113132232-A (hereinafter “Guo”).
Regarding claims 1 and 24, Guo discloses an artificial-intelligence-based (AI-based) system for enabling intelligent orchestration and management of power and energy (e.g. abstract), the AI-based system comprising: memory hardware configured to store instructions; and processor hardware configured to execute the instructions (e.g. pg. 18-20), wherein the instructions include: interfacing with a network including a plurality of nodes (e.g. energy router) (e.g. pg. 18-20); and implementing an adaptive energy data pipeline configured to, communicate data across the network via a first transmission route (e.g. energy routing path) across a first subset of nodes of the plurality of nodes (e.g. pg. 2-3, 7-10 and 15-20), generate a first measurement indicating energy consumption (e.g. energy consumption of 1st energy routing path) across the plurality of nodes at a first time (e.g. pg. 2-3, 7-10 and 15-20), generate a second measurement indicating energy consumption (e.g. energy consumption of optimal energy routing path) across the plurality of nodes at a second time that is after the first time (e.g. pg. 2-3, 7-10 and 15-20), generate a comparison (e.g. via selecting the optimal energy routing path) of the first measurement to the second measurement (e.g. pg. 2-3, 7-10 and 15-20), and based on the comparison, determine a second transmission route (e.g. optimal energy route path) across a second subset of nodes of the plurality of nodes (e.g. pg. 2-3, 7-10 and 15-20), and communicate data across the network via the second transmission route (e.g. pg. 2-3, 7-10 and 15-20), wherein the second transmission route improves energy consumption of communicating data across the network as compared with the first transmission route (e.g. pg. 2-3, 7-10 and 15-20).
Regarding claim 2, Guo discloses the AI-based system of claim 22, wherein the set of data communication parameters includes at least one of, a routing instruction (e.g. pg. 2-3, 7-10 and 15-20), a route parameter (e.g. pg. 2-3, 7-10 and 15-20), an error correction parameter, a compression parameter, a storage parameter, or a timing parameter.
Regarding claim 3, Guo discloses the AI-based platform of claim 1, wherein the adaptive energy data pipeline 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 (e.g. pg. 2-3, 7-10 and 15-20), a quality-of-service (QoS) condition, a usage condition (e.g. pg. 2-3, 7-10 and 15-20), a market factor condition, a user configuration condition.
Regarding claim 7, Guo discloses the AI-based system of claim 1, wherein the adaptive energy data pipeline is further configured to perform one or more of, extracting energy-related data (e.g. pg. 2-3, 7-10 and 15-20), 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 (e.g. pg. 2-3, 7-10 and 15-20), routing and/or transporting energy-related data (e.g. pg. 2-3, 7-10 and 15-20), or maintaining security of energy-related data.
Regarding claim 8, Guo discloses the AI-based system of claim 1, wherein the data is based on one or more public data resources, the public data resources 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 (e.g. price) (e.g. pg. 2-3), or an ecommerce data resource.
Regarding claim 9, Guo discloses the AI-based platform of claim 1, wherein the 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 (e.g. pg. 2-3), customer data, product data, or operating data.
Regarding claim 10, Guo discloses the AI-based system of claim 1, further comprising at least one of an AI-based model or an algorithm is trained based on a training data set (e.g. pg. 2-3, 7-10 and 15-20), wherein the training data set is based on at least one of, one or more human tags, one or more human labels, one or more human interactions with a hardware system, one or more human interactions with a 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 (e.g. pg. 2-3, 7-10 and 15-20).
Regarding claim 11, Guo discloses the AI-based system of claim 1, wherein the adaptive energy data pipeline is configured to orchestrate delivery of energy to one or more points of consumption (e.g. pg. 2-3), and the delivery of the energy includes at least one of, one or more fixed transmission lines (e.g. pg. 2-3), one or more instances of wireless energy transmission, one or more deliveries of fuel, or one or more deliveries of stored energy.
Regarding claim 12, Guo discloses the AI-based system of claim 1, wherein the adaptive -energy data pipeline is configured to record, in a distributed ledger, one or more energy-related events, the one or more energy-related events includes at least one of, an energy purchase event, an energy service charge associated with the energy purchase event, a service charge associated with energy sale event, an energy consumption event (e.g. pg. 2-3, 7-10 and 15-20), 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.
Regarding claim 13, Guo discloses the AI-based system of claim 1, wherein, at least a portion of the adaptive energy data pipeline is deployed in an off grid environment (e.g. pg. 1-3 and 6-7), and the off-grid environment includes at least one of, an off-grid energy generation system (e.g. microgrid) (e.g. pg. 1-3 and 6-7), an off-grid energy storage system, or an off-grid energy mobilization system.
Regarding claims 14 and 25, Guo discloses the AI-based system of claim 1, wherein the adaptive energy data pipeline is configured to, monitor a role of at least one node of the plurality of nodes in an overall energy consumption by at least a portion of the plurality of nodes (e.g. pg. 2-3, 7-10 and 15-20), and based on the monitoring, perform at least one of, managing an energy consumption by the plurality of nodes (e.g. pg. 2-3, 7-10 and 15-20), forecast an energy consumption by the plurality of nodes, or provision resources associated with energy consumption by the plurality of nodes (e.g. pg. 2-3, 7-10 and 15-20).
Regarding claim 15, Guo discloses the AI-based system of claim 1, wherein the plurality of nodes includes a set of edge networking devices, and the set of edge networking devices is configured to 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 (e.g. pg. 2-3, 7-10 and 15-20).
Regarding claim 16, Guo discloses the AI-based system of claim 1, wherein the adaptive energy data pipeline is configured to automatically select a least-cost route for data communicated across the plurality of nodes (e.g. pg. 2-3, 7-10 and 15-20), and the selection is based on a low-priority energy use related to the data (e.g. pg. 2-3, 7-10 and 15-20).
Regarding claim 17, Guo discloses the AI-based system of claim 1, wherein the adaptive energy data pipeline is configured to automatically select a high-quality-of-service route for data communicated across the plurality of nodes, the selection being based on a high-priority energy use related to the data (e.g. pg. 2-3, 7-10 and 15-20).
Regarding claim 18, Guo discloses the AI-based system of claim 1, wherein the adaptive energy data pipeline includes a set of artificial intelligence capabilities (e.g. pg. 2-3, 7-10 and 15-20), and the set of artificial intelligence capabilities is configured to adapt the adaptive energy data pipeline to enable optimization of elements of data transmission in coordination with energy orchestration needs (e.g. pg. 2-3, 7-10 and 15-20).
Regarding claim 19, Guo discloses the AI-based system of claim 1, wherein the adaptive energy data pipeline includes a self-organizing data storage, and the data storage is configured to store data on a device based on at least one of patterns of the data, content of the data, or context of the data (e.g. pg. 2-3, 7-10 and 15-20).
Regarding claim 20, Guo discloses the AI-based system of claim 1, wherein the adaptive energy data pipeline is configured to perform automated, adaptive networking (e.g. pg. 2-3, 7-10 and 15-20), and the adaptive networking includes at least one of, adaptive protocol selection (e.g. ¶256, 263 and 2310), adaptive routing of data based on RF conditions, adaptive filtering of data, adaptive slicing of network bandwidth, adaptive use of cognitive network capacity (e.g. pg. 2-3, 7-10 and 15-20), or adaptive use of peer-to-peer network capacity.
Regarding claims 22 and 26, Guo discloses the AI-based system of claim 1, wherein, a third subset of nodes (e.g. from another energy routing path) is configured, by at least one of a rule or an algorithm, to control a set of data communication parameters associated with the adaptive energy data pipeline (e.g. pg. 2-3, 7-10 and 15-20), and the set of data communication parameters is based on a set of indicators of current network conditions (e.g. cost, energy consumption) in order to optimize energy (via optimal energy routing path) used in the data communication (e.g. pg. 2-3, 7-10 and 15-20).
Regarding claim 23, Guo discloses the AI-based system of claim 22, wherein the set of indicators includes at least one of, an indicator associated with an amount of energy associated with the first transmission route (e.g. pg. 2-3, 7-10 and 15-20), an indicator associated with a cost of energy associated with the first transmission route (e.g. pg. 2-3, 7-10 and 15-20), or an indicator associated with a cost of transmission associated with the first transmission route (e.g. pg. 2-3, 7-10 and 15-20).
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(s) 4-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo as applied to the claims above, and further in view of Miller et al. U.S. PGPub 2022/0066415 (hereinafter “Miller”).
Guo does not explicitly disclose further comprising an adaptive energy digital twin.
Regarding claim 4, Miller discloses the AI-based platform, further comprising an adaptive energy digital twin (e.g. pg. 5, ¶52; pg. 11, ¶87) 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 (e.g. pg. 2, ¶31; pg. 11, ¶88), a stakeholder transportation system, a market condition, or an energy usage priority condition. Regarding claim 5, Miller discloses the AI-based platform, further comprising an adaptive energy digital twin (e.g. pg. 5, ¶52; pg. 11, ¶87) 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 (e.g. pg. 2, ¶31; pg. 11, ¶88), filtering energy data, highlighting energy data, or adjusting energy data. Regarding claim 6, Miller discloses the AI-based platform, further comprising an adaptive energy digital twin (e.g. pg. 5, ¶52; pg. 11, ¶87) that is configured to generate a visual and/or analytic indicator of energy consumption by one or more of, one or more machines (e.g. pg. 2, ¶31; pg. 11, ¶88), one or more factories, or one or more vehicles in a vehicle fleet.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to implement a digital twin to model and simulate environments of an industrial system. One of ordinary skill in the art would have been motivated to do this in order to provide more accurate real-time data analysis of the industrial system.
Therefore, it would have been obvious to modify Guo with Miller to obtain the invention as specified in claims 4-6.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Guo as applied to the claims above, and further in view of King WO-2020154326-A1 (hereinafter “King”).
Guo discloses using a microgrid with renewable energy sources, but does not explicitly disclose using stored energy.
Regarding claim 21, King discloses using stored energy in a microgrid environment (e.g. ¶9, 10 and 22)
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to stored energy. One of ordinary skill in the art would have been motivated to do this as an alternative source of energy that doesn’t depend on grid or weather conditions.
Therefore, it would have been obvious to modify Guo with King to obtain the invention as specified in claim 21.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHARLES R KASENGE whose telephone number is (571)272-3743. The examiner can normally be reached Monday - Friday 7:30am to 4pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kamini Shah can be reached at (571) 272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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CK
September 30, 2025
/CHARLES R KASENGE/Primary Examiner, Art Unit 2116