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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/2/2026 has been entered.
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 Therefore, the rejection has been withdrawn.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 Sampath et al. U.S. PGPub 2017/0237649.
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.
Claim(s) 1-3, 7-12, 14-20 and 22-26 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sampath et al. U.S. PGPub 2017/0237649 (hereinafter “Sampath”).
Regarding claims 1 and 24, Sampath discloses an artificial-intelligence-based (AI-based) system for enabling intelligent orchestration and management of power and energy, the AI-based system comprising: memory hardware configured to store instructions; and processor hardware configured to execute the instructions (e.g. Fig. 1 and 3-6), wherein the instructions include: interfacing with a communication network including a plurality of communication nodes (e.g. network nodes) (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1); and implementing an adaptive energy data pipeline configured to, communicate data across the communication network via a first transmission route (e.g. data path) across a first subset of communication nodes of the plurality of communication nodes (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), generate a first measurement indicating energy consumption (e.g. energy consumption along 1st datapath) across the plurality of communication nodes at a first time (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), generate a second measurement indicating energy consumption (e.g. energy consumption along 2nd datapath) across the plurality of communication nodes at a second time that is after the first time (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), generate a comparison of the first measurement to the second measurement (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), and based on the comparison, determine a second transmission route (e.g. optimal path) across a second subset of communication nodes of the plurality of communication nodes (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), and communicate data across the communication network via the second transmission route (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), wherein the second transmission route improves energy consumption of communicating data across the communication network as compared with the first transmission route (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1).
Regarding claim 2, Sampath 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. ¶11, 23, 28-30, 40 and 54; Fig. 1), a route parameter (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), an error correction parameter, a compression parameter, a storage parameter, or a timing parameter.
Regarding claim 3, Sampath discloses the AI-based platform of claim 1, wherein the adaptive energy data pipeline is further configured to adapt the data communication over the communication network, wherein the adapting is based on one or more of, a congestion condition, a delay and/or latency condition (e.g. ¶11 and 24), a packet loss condition, an error rate condition, a cost of transport condition (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), a quality-of-service (QoS) condition, a usage condition, a market factor condition, a user configuration condition.
Regarding claim 7, Sampath 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. ¶11, 23, 28-30, 40 and 54; Fig. 1), 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. ¶11, 23, 28-30, 40 and 54; Fig. 1), routing and/or transporting energy-related data (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), or maintaining security of energy-related data.
Regarding claim 8, Sampath 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. ¶10, 11 and 23), or an ecommerce data resource.
Regarding claim 9, Sampath 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. ¶11, 23, 28-30, 40 and 54; Fig. 1), customer data, product data, or operating data (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1).
Regarding claim 10, Sampath 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. ¶11, 23, 28-30, 40 and 54; Fig. 1), 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. ¶11, 23, 28-30, 40 and 54; Fig. 1).
Regarding claim 11, Sampath 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. ¶17; Fig. 1), and the delivery of the energy includes at least one of, one or more fixed transmission lines (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), one or more instances of wireless energy transmission (e.g. ¶17; Fig. 1), one or more deliveries of fuel, or one or more deliveries of stored energy.
Regarding claim 12, Sampath 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. ¶11, 23, 28-30, 40 and 54; Fig. 1), 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 claims 14 and 25, Sampath discloses the AI-based system of claim 1, wherein the adaptive energy data pipeline is configured to, monitor a role of at least one communication node of the plurality of communication nodes in an overall energy consumption by at least a portion of the plurality of communication nodes (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), and based on the monitoring, perform at least one of, managing an energy consumption by the plurality of communication nodes (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), forecast an energy consumption by the plurality of communication nodes, or provision resources associated with energy consumption by the plurality of communication nodes (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1).
Regarding claim 15, Sampath discloses the AI-based system of claim 1, wherein the plurality of communication nodes includes a set of edge communication networking devices, and the set of edge communication 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 communication networking devices (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1).
Regarding claim 16, Sampath discloses the AI-based system of claim 1, wherein improving the energy consumption of communicating data across the communication network includes automatically select a least-cost route for data communicated across the plurality of communication nodes (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), and the selection is based on a low-priority energy use related to the data (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1).
Regarding claim 17, Sampath discloses the AI-based system of claim 1, wherein improving the energy consumption of communicating data across the communication network includes automatically select a high-quality-of-service route for data communicated across the plurality of communication nodes, the selection being based on a high-priority energy use related to the data (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1).
Regarding claim 18, Sampath discloses the AI-based system of claim 1, wherein the adaptive energy data pipeline includes a set of artificial intelligence capabilities (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), and improving the energy consumption of communicating data across the communication network includes adapting, by the set of artificial intelligence capabilities, the adaptive energy data pipeline to enable optimization of elements of data transmission in coordination with energy orchestration needs (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1).
Regarding claim 19, Sampath discloses the AI-based system of claim 1, wherein the adaptive energy data pipeline includes a self-organizing data storage, and improving the energy consumption of communicating data across the communication network includes storing, by the self-organizing data storage, 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. ¶11, 23, 28-30, 40 and 54; Fig. 1).
Regarding claim 20, Sampath discloses the AI-based system of claim 1, wherein the adaptive energy data pipeline is configured to perform automated, adaptive communication networking (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), and improving the energy consumption of communicating data across the communication network includes applying, by the adaptive communication networking, 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 communication network bandwidth, adaptive use of cognitive communication network capacity (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), or adaptive use of peer-to-peer communication network capacity.
Regarding claims 22 and 26, Sampath discloses the AI-based system of claim 1, wherein, a third subset of communication 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. ¶11, 23, 28-30, 40 and 54; Fig. 1), and improving the energy consumption of communicating data across the communication network includes selecting the set of data communication parameters based on a set of indicators of current communication network conditions (e.g. cost, energy consumption) (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1).
Regarding claim 23, Sampath 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. ¶11, 23, 28-30, 40 and 54; Fig. 1), an indicator associated with a cost of energy associated with the first transmission route (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1), or an indicator associated with a cost of transmission associated with the first transmission route (e.g. ¶11, 23, 28-30, 40 and 54; Fig. 1).
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 Sampath as applied to the claims above, and further in view of Sha et al. U.S. PGPub 2023/0128173 (hereinafter “Sha”).
Sampath does not explicitly disclose further comprising an adaptive energy digital twin.
Regarding claim 4, Sha discloses the AI-based platform, further comprising an adaptive energy digital twin (e.g. ¶21, 31, 37-38 and 57-59) that represents one or more of, an energy stakeholder entity, an energy distribution resource, a stakeholder information technology, a communication networking infrastructure entity (e.g. ¶21, 31, 37-38 and 57-59), an energy-dependent stakeholder production facility, a stakeholder transportation system, a market condition, or an energy usage priority condition. Regarding claim 5, Sha discloses the AI-based platform, further comprising an adaptive energy digital twin (e.g. ¶21, 31, 37-38 and 57-59) 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. ¶21, 31, 37-38 and 57-59), filtering energy data, highlighting energy data, or adjusting energy data. Regarding claim 6, Sha discloses the AI-based platform, further comprising an adaptive energy digital twin (e.g. ¶21, 31, 37-38 and 57-59) 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. ¶21, 31, 37-38 and 57-59), 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 Sampath with Sha to obtain the invention as specified in claims 4-6.
Claim(s) 13 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sampath as applied to the claims above, and further in view of Horn et al. U.S. PGPub 2020/0059826 (hereinafter “Horn”).
Sampath discloses a data communication network, but does not explicitly disclose the network being powered by an off-grid environment. Sampath discloses consuming energy in a data communication network, but does not explicitly disclose using stored energy.
Regarding claim 13, Horn discloses a system, wherein, at least a portion of the adaptive energy data pipeline is deployed in an off grid environment (e.g. ¶16 and 90), and the off-grid environment includes at least one of, an off-grid energy generation system (e.g. battery, solar panels) (e.g. ¶16 and 90), an off-grid energy storage system (e.g.¶16 and 90), or an off-grid energy mobilization system.
Regarding claim 21, Horn discloses using stored energy in a data communication (e.g. ¶16 and 90).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to use stored energy in an off-grid environment for a data communication network. 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 conditions.
Therefore, it would have been obvious to modify Sampath with King to obtain the invention as specified in claims 13 and 21.
Claim(s) 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sampath as applied to the claims above, and further in view of King WO-2020154326-A1 (hereinafter “King”).
Sampath discloses consuming energy in a data communication network, 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 Sampath with King to obtain the invention as specified in claim 21.
Conclusion
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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CK
August 18, 2026
/CHARLES R KASENGE/Primary Examiner, Art Unit 2116
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