DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim(s) 1-24 is/are pending.
Claim(s) 1, 14 and 24 is/are independent.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) is acknowledged. The prior-filed application is U.S. Provisional Application No. 63/332,160 (filed on 4/18/2022).
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant's cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claim(s) 1 is/are objected to because of the following informalities:
Claim 1 recites in the second line “connected a”, and it should be “connected to a”.
Appropriate correction is required.
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) 1-24 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Fairlie et al. (U.S. Pub. No. 2004/0199294) (hereinafter “Fairlie”).
Regarding claim 1, Fairlie teaches a system for generating hydrogen (Fig. 1 - - hydrogen is produced, i.e. generated)
comprising: one or more processors operatively connected a hydrogen generator capable of being powered by a plurality of different power sources; (Fig. 1, Para. 21-24 - - network controller, i.e. one or more processors, is operatively connected to hydrogen generator which is also powered by energy source; Para. 22, 71 - - energy source can be multiple types or their combinations)
and a non-transitory computer-readable medium storing instructions that, when executed by the one or more processors, cause the one or more processors (Fig. 1, Para. 21-24 - - network controller includes computing means comprising processors and memory storing instructions)
to: receive a first request to generate a first quantity of hydrogen; (Para. 72 - - user define demand, i.e. first request, a certain quantity, i.e. a first quantity, of hydrogen)
select a first one or more power sources of the plurality of different power sources to minimize a cost of generating the first quantity of hydrogen; (Para. 75 - - energy, i.e. power, sources are selected at the lowest cost, i.e. minimize a cost of generating hydrogen)
connect the hydrogen generator to receive power from the first one or more power sources; (Fig. 1, Para. 75 - - hydrogen generator is connected to energy, i.e. power, sources)
and instruct the hydrogen generator to generate the first quantity of hydrogen using the first one or more power sources. (Para. 76 - - controller instructs to provide a minimum quantity of hydrogen to meet the demand, i.e. first quantity of hydrogen)
Regarding claim 2, Fairlie further teaches wherein the first one or more power sources includes at least one renewable power source, (Para. 22, 35, Claims 29, 86, 87, 98, 99 - - at least one renewable power source is used)
and wherein the instructions further cause the one or more processors to: disconnect the hydrogen generator from any non-renewable power sources; (Para. 41 - - high carbon content sources, i.e. non-renewable power sources, are sequestered, i.e. disconnected)
and instruct the hydrogen generator to generate additional hydrogen to fill at least one storage tank using the at least one renewable power source. (Para. 41 - - storage tank is filled using produced hydrogen using low carbon producing energy sources, i.e. renewable power sources; Claims 87, 99 - - primary energy source is renewable resource)
Regarding claim 3, Fairlie further teaches wherein the at least one renewable power source is selected from the group consisting of solar, wind, geothermal, and hydropower. (Para. 22, Claims 46, 86, 87, 98, 99 - - solar, wind and hydro can be used)
Regarding claim 4, Fairlie further teaches wherein the instructions further cause the one or more processors to store historical data relating to the cost of generating the first quantity of hydrogen. (Claims 104, 105, 113 - - historical data is related to energy cost for generating first quantity of hydrogen)
Regarding claim 5, Fairlie further teaches wherein the historical data includes an indication of the first quantity. (Claims 104, 113 - - historical data includes hydrogen demand data, i.e. first quantity of hydrogen needed)
Regarding claim 6, Fairlie further teaches wherein the historical data includes one or more of a date and time during which the first quantity of hydrogen is generated. (Para. 81 - - controller includes stored data related to information like time of commencement and duration, i.e. data and time, during which first quantity of hydrogen is generated)
Regarding claim 7, Fairlie further teaches wherein the historical data includes price data for one or more of the plurality of different power sources. (Claims 104, 105, 113 - - data includes cost, i.e. price, for energy, i.e. power sources)
Regarding claim 8. The system of claim 4, wherein the historical data includes weather data for a period of time during which the first quantity of hydrogen is generated. (Para. 24 - - geographic location data used)
Regarding claim 9. The system of claim 8, wherein the weather data is selected from the group consisting of a UV index, a level of cloud cover, and a wind speed. (Para. 24 - - geographic location data used)
Regarding claim 10, Fairlie further teaches wherein to store the historical data relating to the cost of generating the first quantity of hydrogen includes training a machine learning system with the historical data. (Para. 24 - - future demand is predicted based on present/historical data using standard projection models, i.e. machine learning)
Regarding claim 11. The system of claim 10, wherein the machine learning system includes a neural network. (Para. 24 - - future demand is predicted based on present/historical data using standard projection models, i.e. machine learning)
Regarding claim 12, Fairlie further teaches wherein the instructions further cause the one or more processors to: receive a second request to generate a second quantity of hydrogen; (Fig. 1, Para. 72 - - multiple users demand hydrogen, i.e. additional user defines demand, i.e. request, for additional, i.e. second, quantity of hydrogen)
and use the trained machine learning system to optimize selection a second one or more power sources of the plurality of different power sources to minimize a cost of generating the second quantity of hydrogen. (Para. 24 - - future demand is predicted based on present/historical data using standard projection models, i.e. machine learning; Para. 75 - - energy, i.e. power, sources are selected at the lowest cost, i.e. minimize a cost of generating hydrogen)
Regarding claim 13, Fairlie further teaches wherein to use the trained machine learning system includes providing as input to the machine learning system at least one of an indication of the second quantity, price data for one or more of the plurality of different power sources, one or more of a date and time of the second request, and a weather forecast. (Para. 24 - - network data is linked to model, i.e. machine learning system; Para. 22 - - network data includes demand by user(s), i.e. where multiple users include indication of second quantity, time and duration of delivery, i.e. date and time of second request)
where claims 14 to 24 are process and computer-readable medium claims corresponding to system claims 1-13 above
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Saad M. Kabir whose telephone number is 571-270-0608 (direct fax number is 571-270-9933). The examiner can normally be reached on Mondays to Fridays 9am to 5pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached on 571-272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SAAD M KABIR/
Examiner, Art Unit 2119
/MOHAMMAD ALI/Supervisory Patent Examiner, Art Unit 2119