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 8/14/2025, with respect to the rejection(s) of the claim(s) under 35 U.S.C. 103 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 Therefore, the rejection has been withdrawn.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 Janous et al. U.S. PGPub 2016/0011618.
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) 1, 4, 5, 7-9, 12, 14 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Janous et al. U.S. PGPub 2016/0011618 (hereinafter “Janous”) in view of Brooks et al. U.S. PGPub 2022/0083027 (hereinafter “Brooks”).
Regarding claim 1, Janous discloses an artificial intelligence-based (AI-based) system for enabling intelligent orchestration and management of power and energy (e.g. ¶84-86), the AI-based system comprising: memory hardware configured to store instructions (e.g. ¶24-33; Fig. 1); and processor hardware configured to execute the instructions (e.g. ¶24-33; Fig. 1), wherein the instructions include, implementing a neural network algorithm configured to, orchestrate delivery of energy to one or more points of consumption (e.g. server installations, electrical consumers) (e.g. ¶24-26, 35, 48-49 and 54-56; Fig. 1-6), and adjust the delivery of energy to the one or more points of consumption based on (e.g. ¶18-21, 65, 83, 93, 103 and 112), a probability of a deficiency (e.g. probability of grid failure) of available energy at the one or more points of consumption (e.g. ¶18-21, 65, 76, 93-99, 103 and 112), and a consequence of the deficiency of available energy (e.g. no power to desired consumption point) at the one or more points of consumption (e.g. ¶18-21, 65, 76, 93-99, 103 and 112). Janous discloses using a neural network algorithm for predicting conditions of an industrial environment and controlling an industrial environment (e.g. ¶24-33 and 84-86), but does not explicitly disclose utilizing a digital twin.
Brooks discloses predicting and controlling an industrial environment via a digital twin (e.g. ¶55).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to utilize a digital twin for a power management environment. One of ordinary skill in the art would have been motivated to do this since the digital twin allows for real-time data updates for simulating a physical system which would result in an improved, more accurate model.
Therefore, it would have been obvious to modify Janous with Brooks to obtain the invention as specified in claims 1, 4, 5, 7-9, 12, 14 and 22.
Regarding claim 4, Janous discloses the AI-based system of claim 1, wherein the digital model represents at least one of, an energy stakeholder entity, an energy distribution resource (e.g. ¶24-33; Fig. 1), 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 (e.g. ¶83).
Regarding claim 5, Janous discloses the AI-based system of claim 1, wherein the digital model is configured to perform at least one of, providing at least one of a visual or an analytic indicator of energy consumption (i.e. grid conditions) by one or more energy consumers (e.g. ¶7, 25-27 and 112), filtering energy data, highlighting energy data, adjusting energy data (e.g. ¶18-21, 65, 83, 93, 103 and 112), or generating a visual or an analytic indicator of energy consumption by at least one of (e.g. ¶7, 25-27 and 112), one or more machines (e.g. Fig. 1-4), one or more factories (e.g. Fig. 1-4), or one or more vehicles in a vehicle fleet (e.g. Fig. 1-4).
Regarding claim 7, Janous discloses the AI-based system of claim 1, wherein the digital model includes at least one AI-based model or algorithm that is trained based on a training data set (e.g. ¶84-90 and 115-117), and 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 (e.g. ¶84-90 and 115-117) (e.g. ¶84-90 and 115-117), one or more AI-generated training data samples, a supervised learning training process (e.g. ¶84-90 and 115-117), a semi-supervised learning training process, or a deep learning training process.
Regarding claim 8, Janous discloses the AI-based system of claim 1, wherein the digital model is configured to orchestrate delivery of energy to one or more points of consumption (e.g. ¶24-26, 35, 48-49 and 54-56; Fig. 1-6), and the delivery of the energy includes at least one of, one or more fixed transmission lines (e.g. ¶24-26, 35, 48-49 and 54-56; Fig. 1-6), one or more instances of wireless energy transmission, one or more deliveries of fuel, or one or more deliveries of stored energy (e.g. ¶24-26, 35, 48-49 and 54-56; Fig. 1-6).
Regarding claim 9, Janous discloses the AI-based system of claim 8, wherein the digital model is further configured to adjust the delivery of energy to the one or more points of consumption based on an at least one of energy delivery or consumption policy (e.g. ¶18-21, 65, 83, 93, 103 and 112).
Regarding claim 12, Janous discloses the AI-based system of claim 8, wherein the digital model is configured to determine the delivery of energy to the one or more points of consumption based on a comparison of energy availability at each of two or more energy sources (e.g. ¶18-21, 65, 83, 93, 103 and 112), wherein the comparison includes at least one of, a current quantity of energy stored by at least one of the two or more energy sources, a future quantity of energy stored by at least one of the two or more energy sources, a current resource expenditure associated with at least one of acquiring, storing, or delivering the energy by at least one of the two or more energy sources (e.g. ¶18-21, 65, 83, 93, 103 and 112), a future resource expenditure associated with at least one of acquiring, storing, or delivering the energy by at least one of the two or more energy sources, a current demand by other energy consumers for the energy of at least one of the two or more energy sources (e.g. ¶18-21, 65, 83, 93, 103 and 112), or a future demand by other energy consumers for the energy of at least one of the two or more energy sources (e.g. ¶18-21, 65, 83, 93, 103 and 112).
Regarding claim 14, Janous discloses the AI-based system of claim 1, wherein, the digital algorithm is deployed in an off-grid environment (e.g. ¶36-39; Fig. 2), and the off-grid environment includes at least one of, an off-grid energy generation system (e.g. ¶36-39; Fig. 2), an off-grid energy storage system, or an off-grid energy mobilization system.
Claim(s) 10 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Janous and Brooks as applied to the claims above, and further in view of Manikfan et al. U.S. PGPub 2022/0247188 (hereinafter “Manikfan”).
Regarding claim 10, Janous does not disclose determining a carbon generation and/or emissions effect of the delivery of energy to points of consumption. Regarding claim 13, Janous discloses recording energy-related events (e.g. ¶18-21, 65, 83, 93, 103 and 112), but does not explicitly disclose recording energy-related events in a distributed ledger or blockchain.
Manikfan discloses determining a carbon generation and/or emissions effect of the delivery of energy to points of consumption (e.g. pg. 6, ¶84; pg. 7, ¶99-106; pg. 9, ¶122-124). Manikfan discloses recording energy-related events in a distributed ledger or blockchain, wherein the energy-related events include a carbon emission production event (e.g. pg. 7, ¶97-98). Manikfan discloses energy generation sources being off-grid (e.g. pg. 1, ¶9-10).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to determine carbon generation or emissions effects, record carbon emission events on a blockchain and incorporate an off-grid energy generation source for Janous’s invention. One of ordinary skill in the art would have been motivated to do this in order to track and document permanently environmental effects of managing the power delivery and additionally provide the ability to deliver power to a desired location independent of the electrical grid.
Therefore, it would have been obvious to modify Janous and Brooks with Manikfan to obtain the invention as specified in claims 10 and 13.
Allowable Subject Matter
Claims 2, 3, 6, 15-21 and 23-25 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
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.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
CK
November 6, 2025
/CHARLES R KASENGE/Primary Examiner, Art Unit 2116