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 .
Status of Claims
This action is in reply to the communications filed on 01/24/2024.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 02/10/2025, 05/06/2025, 03/10/2026, and 04/15/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 10 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 10 recites “training the hydrogen production safety prediction machine learning model…”. There is insufficient antecedent basis in the claim for the hydrogen production safety prediction machine learning model. Therefore, claim 10 is rendered indefinite for reciting a limitation for which there is a lack of antecedent basis. For the sake of compact prosecution, this limitation will be interpreted as “training a hydrogen production safety prediction machine learning model…”.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
First of all, claims must be directed to one or more of the following statutory categories: a process, a machine, a manufacture, or a composition of matter. Claims 1-8 are directed to a machine (“apparatus”), claims 9-15 are directed to a process (“computer-implemented method”), and claim 16-20 are directed to a manufacture (“non-transitory computer-readable storage medium”). Thus, claims 1-20 satisfy Step One because they are all within one of the four statutory categories of eligible subject matter. Claims 1-20, however, are directed to an abstract idea without significantly more.
Regarding independent claim 1, the specific limitations that recite an abstract idea are:
Receive a plurality of runtime hydrogen production variable indicators from […];
Generate at least one predicted hydrogen production operation indicator based at least in part on […];
Determine whether the at least one predicted hydrogen production operation indicator satisfies at least one corresponding hydrogen production operation threshold indicator associated with the hydrogen production facility; and
I In response to determining that the at least one predicted hydrogen production operation indicator does not satisfy the at least one corresponding hydrogen production operation threshold indicator: generate an adjusted hydrogen production variable indicator corresponding to a runtime hydrogen production variable indicator of the plurality of runtime hydrogen production variable indicators;
Therefore, claims 1 and 2-8, by virtue of dependence, recite certain methods of organizing human activity. In particular, the limitations of claim 1 identified above, as a whole, recite concepts of mental processes. In particular, the limitation identified above recite concepts of collecting information, analyzing information, comparing information, and displaying a particular result of the analysis of information. See MPEP 2106.04(a)(2)(III). Furthermore, the limitations identified above recite concepts of managing costs corresponding to the production of commercial products (i.e., hydrogen) – which is the abstract idea of facilitating commercial interactions. See MPEP 2106.04(a)(2)(II). This is further evidenced by the Specification at ¶ [0046] (“example types of predicted hydrogen production operation indicators may include, but are not limited to, […] predicted hydrogen production cost indicators, and/or the like”) and ¶ [0151] (“adjusting the hydrogen production variable indicators such that the predicted hydrogen production operation indicator satisfies the corresponding hydrogen production operation threshold indicator, which may […] reduce the cost associated with manufacturing hydrogen”).
The judicial exception recited above is not integrated into a practical application. The additional elements of the claim include “an apparatus comprising at least one processor and at least one non-transitory memory comprising a computer program code, the at least one non-transitory memory and the computer program code configured to, with the at least one processor, cause the apparatus to” perform the claim limitations, “a hydrogen production control system associated with a hydrogen production facility”, steps for “inputting the plurality of runtime hydrogen production variable indicators to one or more hydrogen production machine learning models”, and steps for “transmit[ting] the adjusted hydrogen production variable indicator to the hydrogen control system”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer tools and instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements, in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A). Furthermore, the additional elements involving steps for transmitting information over a network fail to amount to significantly more than the judicial exception because the courts have found transmitting information over a network to be well-understood, routine, and conventional activities. See MPEP 2106.05(d)(II). Because the invention is merely reciting well-understood, routine, and conventional activity, the additional elements of this claim which involve transmitting information over a network, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. Thus, claim 1 is not patent eligible.
Regarding independent claim 9, the specific limitations that recite an abstract idea are:
Receiving a plurality of runtime hydrogen production variable indicators from […];
Generating at least one predicted hydrogen production operation indicator based at least in part on […];
Determining whether the at least one predicted hydrogen production operation indicator satisfies at least one corresponding hydrogen production operation threshold indicator associated with the hydrogen production facility; and
In response to determining that the at least one predicted hydrogen production operation indicator does not satisfy the at least one corresponding hydrogen production operation threshold indicator: generating an adjusted hydrogen production variable indicator corresponding to a runtime hydrogen production variable indicator of the plurality of runtime hydrogen production variable indicators;
Therefore, claims 9 and 10-15, by virtue of dependence, recite certain methods of organizing human activity. In particular, the limitations of claim 9 identified above, as a whole, recite concepts of mental processes. In particular, the limitation identified above recite concepts of collecting information, analyzing information, comparing information, and displaying a particular result of the analysis of information. See MPEP 2106.04(a)(2)(III). Furthermore, the limitations identified above recite concepts of managing costs corresponding to the production of commercial products (i.e., hydrogen) – which is the abstract idea of facilitating commercial interactions. See MPEP 2106.04(a)(2)(II). This is further evidenced by the Specification at ¶ [0046] (“example types of predicted hydrogen production operation indicators may include, but are not limited to, […] predicted hydrogen production cost indicators, and/or the like”) and ¶ [0151] (“adjusting the hydrogen production variable indicators such that the predicted hydrogen production operation indicator satisfies the corresponding hydrogen production operation threshold indicator, which may […] reduce the cost associated with manufacturing hydrogen”).
The judicial exception recited above is not integrated into a practical application. The additional elements of the claim include “a hydrogen production control system associated with a hydrogen production facility”, steps for “inputting the plurality of runtime hydrogen production variable indicators to one or more hydrogen production machine learning models”, and steps for “transmitting the adjusted hydrogen production variable indicator to the hydrogen control system”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer tools and instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements, in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A). Furthermore, the additional elements involving steps for transmitting information over a network fail to amount to significantly more than the judicial exception because the courts have found transmitting information over a network to be well-understood, routine, and conventional activities. See MPEP 2106.05(d)(II). Because the invention is merely reciting well-understood, routine, and conventional activity, the additional elements of this claim which involve transmitting information over a network, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. Thus, claim 9 is not patent eligible.
Regarding independent claim 16, the specific limitations that recite an abstract idea are:
Receive a plurality of runtime hydrogen production variable indicators from […];
Generate at least one predicted hydrogen production operation indicator based at least in part on […];
Determine whether the at least one predicted hydrogen production operation indicator satisfies at least one corresponding hydrogen production operation threshold indicator associated with the hydrogen production facility; and
In response to determining that the at least one predicted hydrogen production operation indicator does not satisfy the at least one corresponding hydrogen production operation threshold indicator: generate an adjusted hydrogen production variable indicator corresponding to a runtime hydrogen production variable indicator of the plurality of runtime hydrogen production variable indicators;
Therefore, claims 16 and 17-20, by virtue of dependence, recite certain methods of organizing human activity. In particular, the limitations of claim 16 identified above, as a whole, recite concepts of mental processes. In particular, the limitation identified above recite concepts of collecting information, analyzing information, comparing information, and displaying a particular result of the analysis of information. See MPEP 2106.04(a)(2)(III). Furthermore, the limitations identified above recite concepts of managing costs corresponding to the production of commercial products (i.e., hydrogen) – which is the abstract idea of facilitating commercial interactions. See MPEP 2106.04(a)(2)(II). This is further evidenced by the Specification at ¶ [0046] (“example types of predicted hydrogen production operation indicators may include, but are not limited to, […] predicted hydrogen production cost indicators, and/or the like”) and ¶ [0151] (“adjusting the hydrogen production variable indicators such that the predicted hydrogen production operation indicator satisfies the corresponding hydrogen production operation threshold indicator, which may […] reduce the cost associated with manufacturing hydrogen”).
The judicial exception recited above is not integrated into a practical application. The additional elements of the claim include “a hydrogen production control system associated with a hydrogen production facility”, steps for “inputting the plurality of runtime hydrogen production variable indicators to one or more hydrogen production machine learning models”, and steps for “transmit[ting] the adjusted hydrogen production variable indicator to the hydrogen control system”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer tools and instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
Finally, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above, the additional elements, in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A). Furthermore, the additional elements involving steps for transmitting information over a network fail to amount to significantly more than the judicial exception because the courts have found transmitting information over a network to be well-understood, routine, and conventional activities. See MPEP 2106.05(d)(II). Because the invention is merely reciting well-understood, routine, and conventional activity, the additional elements of this claim which involve transmitting information over a network, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. Thus, claim 16 is not patent eligible.
Claim 2 further describes the plurality of runtime hydrogen production variable indicators as comprising a production power source variable indicator, a hydrogen production quantity variable indicator, a hydrogen storage location variable indicator, and a hydrogen transport plan variable indicator. Thus, claim 2 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 1 from which the claim depends.
Claim 3 further describes the predicted hydrogen production operation indicator as comprising a predicted hydrogen production safety indicator. Thus, claim 3 further describes the abstract idea.
The claim further introduces the additional elements of “wherein the one or more hydrogen production machine learning models comprise a hydrogen production safety prediction machine learning model”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer tools on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 4 further describes receiving a plurality of historical hydrogen production variable indicators associated with the hydrogen production facility and receiving a plurality of historical hydrogen production safety indicators associated with the hydrogen production facility. Thus, claim 4 further describes the abstract idea.
The claim further introduces the additional elements of “train[ing] the hydrogen production safety prediction machine learning model based at least in part on the plurality of historical hydrogen production variable indicators and the plurality of historical hydrogen production safety indicators”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer tools and instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 5 further describes the predicted hydrogen production operation indicator as comprising a predicted hydrogen production cost indicator. Thus, claim 5 further describes the abstract idea.
The claim further introduces the additional elements of “wherein the one or more hydrogen production machine learning models comprise a hydrogen production cost prediction machine learning model”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer tools on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 6 further describes receiving a plurality of historical hydrogen production variable indicators associated with the hydrogen production facility, and receiving a plurality of historical hydrogen production cost indicators associated with the hydrogen production facility. Thus, claim 6 further describes the abstract idea.
The claim further introduces the additional elements of “train[ing] the hydrogen production cost prediction machine learning model based at least in part on the plurality of historical hydrogen production variable indicators and the plurality of historical hydrogen production cost indicators”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer tools and instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 7 further describes the runtime hydrogen production variable indicators as comprising the runtime hydrogen production variable indicators and one or more additional runtime hydrogen production variable indicators. Thus, claim 7 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 1 from which the claim depends.
Claim 8 further describes generating a predicted hydrogen production operation indicator and determining whether the predicted hydrogen production operation indicator satisfies at least one corresponding hydrogen production operation threshold indicator. Thus, claim 8 further describes the abstract idea.
The claim further introduces the additional elements of performing the claim limitations “prior to transmitting the adjusted hydrogen production variable indicator”, steps for “inputting the adjusted hydrogen production variable indicator and the one or more additional runtime hydrogen production variable indicators to the one or more hydrogen production machine learning models”, and steps for “in response to determining that the predicted hydrogen production operation indicator satisfies the at least one corresponding hydrogen production operation threshold indicator, transmit the adjusted hydrogen production variable indicator to the hydrogen production control system”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer tools and instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 10 further describes receiving a plurality of historical hydrogen production variable indicators associated with the hydrogen production facility and receiving a plurality of historical hydrogen production safety indicators associated with the hydrogen production facility. Thus, claim 10 further describes the abstract idea.
The claim further introduces the additional elements of “training the hydrogen production safety prediction machine learning model based at least in part on the plurality of historical hydrogen production variable indicators and the plurality of historical hydrogen production safety indicators”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer tools and instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 11 further describes the plurality of runtime hydrogen production variable indicators as comprising a production power source variable indicator, a hydrogen production quantity variable indicator, a hydrogen storage location variable indicator, and a hydrogen transport plan variable indicator. Thus, claim 2 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 9 from which the claim depends.
Claim 12 further describes the predicted hydrogen production operation indicator as comprising a predicted hydrogen production cost indicator. Thus, claim 12 further describes the abstract idea.
The claim further introduces the additional elements of “wherein the one or more hydrogen production machine learning models comprise a hydrogen production cost prediction machine learning model”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer tools on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 13 further describes receiving a plurality of historical hydrogen production variable indicators associated with the hydrogen production facility and receiving a plurality of historical hydrogen production cost indicators associated with the hydrogen production facility. Thus, claim 13 further describes the abstract idea.
The claim further introduces the additional elements of “training the hydrogen production cost prediction machine learning model based at least in part on the plurality of historical hydrogen production variable indicators and the plurality of historical hydrogen production cost indicators”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer tools and instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 14 further describes the runtime hydrogen production variable indicators as comprising the runtime hydrogen production variable indicator and one or more additional runtime hydrogen production variable indicators. Thus, claim 14 further describes the abstract idea. The claim does not recite any further additional elements beyond the additional elements previously addressed with regard to claim 9 from which the claim depends.
Claim 15 further describes generating a predicted hydrogen production operation indicator and determining whether the predicted hydrogen production operation indicator satisfies at least one corresponding hydrogen production operation threshold indicator. Thus, claim 8 further describes the abstract idea.
The claim further introduces the additional elements of steps for “inputting the adjusted hydrogen production variable indicator and the one or more additional runtime hydrogen production variable indicators to the one or more hydrogen production machine learning models”, and steps for “in response to determining that the predicted hydrogen production operation indicator satisfies the at least one corresponding hydrogen production operation threshold indicator, transmit the adjusted hydrogen production variable indicator to the hydrogen production control system”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer tools and instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 17 further describes the predicted hydrogen production operation indicator as comprising a predicted hydrogen production safety indicator. Thus, claim 16 further describes the abstract idea.
The claim further introduces the additional elements of “wherein the one or more hydrogen production machine learning models comprise a hydrogen production safety prediction machine learning model”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer tools on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 18 further describes receiving a plurality of historical hydrogen production variable indicators associated with the hydrogen production facility and receiving a plurality of historical hydrogen production safety indicators associated with the hydrogen production facility. Thus, claim 18 further describes the abstract idea.
The claim further introduces the additional elements of “train[ing] the hydrogen production safety prediction machine learning model based at least in part on the plurality of historical hydrogen production variable indicators and the plurality of historical hydrogen production safety indicators”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer tools and instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 19 further describes the predicted hydrogen production operation indicator as comprising a predicted hydrogen production cost indicator. Thus, claim 19 further describes the abstract idea.
The claim further introduces the additional elements of “wherein the one or more hydrogen production machine learning models comprise a hydrogen production cost prediction machine learning model”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer tools on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(A).
Claim 20 further describes receiving a plurality of historical hydrogen production variable indicators associated with the hydrogen production facility and receiving a plurality of historical hydrogen production cost indicators associated with the hydrogen production facility. Thus, claim 20 further describes the abstract idea.
The claim further introduces the additional elements of “train[ing] the hydrogen production cost prediction machine learning model based at least in part on the plurality of historical hydrogen production variable indicators and the plurality of historical hydrogen production cost indicators”. The abstract idea is not integrated into a practical application because the additional elements merely serve as generic computer tools and instructions on which the abstract idea is implemented. See MPEP 2106.05(f).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, either alone or in combination, are recited at a high level of generality such that they amount to no more than mere instructions to apply the abstract idea using generic computer components. Because merely “applying” the exception using generic computer components/instructions cannot provide an inventive concept, the additional elements, when viewed as a whole/ordered combination, do not recite significantly more than the judicial exception. See MPEP 2106.05(I)(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.
Claims 1-20 are rejected under 35 U.S.C. § 102(a)(2) as being anticipated by Cella et al. U.S. Publication No. 2023/0281527 (“Cella”).
Claim 1: Cella teaches the following:
An apparatus comprising at least one processor and at least one non-transitory memory comprising a computer program code, the at least one non-transitory memory and the computer program code configured to, with the at least one processor, cause the apparatus to: (¶ [0996]: one or more non-transitory computer-readable media comprising computer executable instructions that, when executed, may cause at least one processor to perform actions disclosed).
Receive a plurality of runtime hydrogen production variable indicators from a hydrogen production control system associated with a hydrogen production facility; (¶ [0014]: methods and systems for using collected data to provide improved monitoring, control, intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments); (¶ [1104]: an industrial system includes any large scale process system, mechanical system, chemical system, assembly line, oil and gas system, and energy generation); (¶ [1688]: intelligent industrial equipment and systems may be configured in various networks. Smart heating system includes a smart hydrogen production system and a smart hydrogen storage system, and may be configured individually or as an integral system connected as one or more nodes in a network of industrial equipment and systems); (¶[1885]: systems related to hydrogen production, storage, distribution and use may include, be associated with, or integrate improvement features); (¶ [0067]: a data collection and processing system includes variable groups of sensor inputs, each of the variable groups of sensor inputs operationally coupled to an industrial environment); (¶ [0097]: a monitoring system for collecting data related to an industrial environment including a data collector communicatively coupled to a plurality of input channels. The plurality of input channels comprises data relating to an aspect of an industrial production process).
Generate at least one predicted hydrogen production operation indicator based at least in part on inputting the plurality of runtime hydrogen production variable indicators to one or more hydrogen production machine learning models; (¶ [0097]: see above); (¶ [0390]: the platform may include the local data collection system deployed in the environment using machine learning to enable derivation-based learning outcomes. Platform may, therefore, learn from and make decisions on a set of data, by making data-driven predictions and adapting according to the set of data); (¶ [0073]: The system further includes reporting a future state of the industrial environment); (¶ [0074]: the controller is configured to determine a noise value from one or more of an ambient noise, a local noise, or a vibration noise. The controller is further configured to predict a state of at least one of a component or a process of the industrial environment in response to the determined noise value); (¶ [4120]: digital twin simulation system may recursively simulate impacts of the one or more states until achieving a desired fit, provide the simulated values to the cognitive intelligence system for evaluation and determination of potential actions, receive the potential actions, evaluate impacts of each of the potential actions for a respective desired fit (e.g., cost functions for minimizing production disturbance, preserving critical components, minimizing maintenance and/or downtime, optimizing system, worker, user, or personal safety, etc.))
Determine whether the at least one predicted hydrogen production operation indicator satisfies at least one corresponding hydrogen production operation threshold indicator associated with the hydrogen production facility; (¶ [4120]: see above); (¶ [0590]: multiple sensors may be arranged into a set of sensors for condition-specific monitoring. Each set may be selected to provide information about elements in an industrial environment that may provide insight into potential problems, root causes of problems, and the like. Each set may be associated with a condition that may be monitored for compliance with an acceptable range of values); (¶ [0394]: Methods and systems are disclosed herein for cloud-based, machine pattern analysis of state information from multiple industrial sensors to provide anticipated state information for an industrial system. Machine learning may take advantage of a state machine, such as tracking states of multiple analog and/or digital sensors, and determining anticipated states of the industrial system based on historical data. For example, where a temperature state of an industrial machine exceeds a certain threshold and is followed by a fault condition, such as breaking down of a set of bearings, that temperature state may be tracked by a pattern recognizer, which may produce an output data structure indicating an anticipated bearing fault state (whenever an input state of a high temperature is recognized). A wide range of measurement values and anticipated states may be managed by a state machine, relating to temperature, pressure, vibration, and many others); (¶ [4121]: digital twin simulation system and the cognitive intelligence system may repeatedly share and update the simulated values and response actions for each desired outcome until desired conditions are met (e.g., convergence for each evaluated cost function for each evaluated action)).
In response to determining that the at least one predicted hydrogen production operation indicator does not satisfy the at least one corresponding hydrogen production operation threshold indicator: generate an adjusted hydrogen production variable indicator corresponding to a runtime hydrogen production variable indicator of the plurality of runtime hydrogen production variable indicators; (¶ [0394]: see above); (¶ [0590]: see above); (¶ [4120]-¶[4121]: see above); (¶ [0102]: method further includes providing an adjustment recommendation in response to a signal effectiveness of at least one of the plurality of input channels relative to the state value. The method further includes providing the adjustment recommendation in response to a predictive confidence of at least one of the plurality of input channels relative to the state value. The method further includes providing the adjustment recommendation in response to a predictive accuracy of at least one of the plurality of input channels relative to the state value); (¶ [0097]: provide the adjustment recommendation as one of an equipment change for a component of the industrial production process, or an equipment operating parameter change for the component of the industrial production process); (¶ [0102]: adjusting the industrial production process comprises rebalancing process loads between components of the industrial production process to achieve at least one of: extending a life of one of a plurality of components of the industrial production process, improving a probability of success of the industrial production process, or facilitating maintenance on one of the plurality of components of the industrial production process); (¶ [0106]: the process parameter change comprises a command to rebalance process loads between components of the industrial production process).
Transmit the adjusted hydrogen production variable indicator to the hydrogen production control system. (¶ [0102]: see above); (¶ [1793]: control module may control one or more aspects of an industrial setting based on a determination made by the AI system. The control module may be configured to provide commands to a device or system at the industrial setting to take a remedial action in response to a particular issue being detected); (¶[1708]: the edge system may include, link or connect to, integrate with, or be integrated into a control system, such as for providing control for one or more industrial entities, such as controlling a machine in a factory, controlling a workflow, or controlling sub-systems, systems, or operations of an entire factory or set of factories).
Claim 2: Cella teaches the limitations of claim 1. Furthermore, Cella teaches the following:
Wherein the plurality of runtime hydrogen production variable indicators comprises a production power source variable indicator, a hydrogen production quantity variable indicator, a hydrogen storage location variable indicator, and a hydrogen transport plan variable indicator. (¶ [0077]: controller is further configured to implement a self-organizing data marketplace including the data from the variable groups of sensor inputs. Controller is further configured to train an artificial intelligence (AI) model based on industry-specific feedback relating to the industrial environment); (¶ [1034]: feedback may include utilization measures, efficiency measures (e.g., power or energy utilization, use of storage, use of bandwidth, use of input/output use of perishable materials, use of fuel, and/or financial efficiency), productivity measures (e.g., workflow), yield measures, and profit measures. Parameters may include: storage parameters (e.g., data storage, fuel storage, storage of inventory and the like), location and positioning parameters (e.g., , location of power sources and the like)); (¶ [0403]: For data involving pricing, a data transaction system may track orders, delivery, and utilization, including fulfillment of orders).
Claim 3: Cella teaches the limitations of claim 1. Furthermore, Cella teaches the following:
Wherein the at least one predicted hydrogen production operation indicator comprises a predicted hydrogen production safety indicator; (¶[1885]: systems related to hydrogen production, storage, distribution and use may include, be associated with, or integrate improvement features. Improvement features may include process control and heat recovery, flow control and precision control, safety, reliability and greater service availability. Other features that may be provided and/or be integrated with the hydrogen-based systems described herein may include data collection, analysis, modeling for improvement, and monitoring and analysis to facilitate preventive maintenance and repair); (¶ [4120]: digital twin system employs the digital twin simulation system to simulate one or more impacts, such as immediate, upstream, downstream, and/or continuing effects, of recognized states. The digital twin simulation system may recursively simulate impacts of the one or more states until achieving a desired fit, provide the simulated values to the cognitive intelligence system for evaluation and determination of potential actions, receive the potential actions, evaluate impacts of each of the potential actions for a respective desired fit (e.g., cost functions for minimizing production disturbance, optimizing system, worker, user, or personal safety, etc.).
Wherein the one or more hydrogen production machine learning models comprise a hydrogen production safety prediction machine learning model. (¶ [4120] see above); (¶ [0409]: a platform is provided having training AI models based on industry-specific feedback. The various embodiments of cognitive systems disclosed herein may take inputs and feedback from industry-specific and domain-specific sources. Thus, learning and adaptation of storage organization, network usage, combination of sensor and input data, data pooling, data packaging, data pricing, and other features may be configured by learning on the domain-specific feedback measures of a given environment or application, such as an application involving IoT devices (such as an industrial environment). This may include optimization of benefits (such as relating to safety, satisfaction, health), and others); (¶ [0077]: Controller is further configured to train an artificial intelligence (AI) model based on industry-specific feedback relating to the industrial environment).
Claim 4: Cella teaches the limitations of claim 3. Furthermore, Cella teaches the following:
Receive a plurality of historical hydrogen production variable indicators associated with the hydrogen production facility; (¶ [0394]: systems are disclosed herein for cloud-based, machine pattern analysis of state information from multiple industrial sensors to provide anticipated state information for an industrial system. Machine learning may take advantage of a state machine, such as tracking states of multiple analog and/or digital sensors, feeding the states into a pattern analysis facility, and determining anticipated states of the industrial system based on historical data about sequences of state information).
Receive a plurality of historical hydrogen production safety indicators associated with the hydrogen production facility; (¶ [0394]: see above); (¶[1028]: Intelligent management of data collection via smart bands may improve various parameters of data collection, such as safety parameters); (¶[1569]: messages relating to a critical fault condition of a machine (e.g., overheating, excessive vibration, or any of the other fault conditions described throughout this disclosure) or relating to a safety hazard may be designated as time critical or may be learned to be time-critical by the expert system, such as based on feedback regarding outcomes over time, including outcomes for similar machines having similar data in similar industrial environments).
Train the hydrogen production safety prediction machine learning model based at least in part on the plurality of historical hydrogen production variable indicators and the plurality of historical hydrogen production safety indicators. (¶ [00394]: see above); (¶ [1028]: see above); (¶ [1569]: see above); (¶ [0409]: a platform is provided having training AI models based on industry-specific feedback. The various embodiments of cognitive systems disclosed herein may take inputs and feedback from industry-specific and domain-specific sources. Thus, learning and adaptation of storage organization, network usage, combination of sensor and input data, data pooling, data packaging, data pricing, and other features may be configured by learning on the domain-specific feedback measures of a given environment or application, such as an application involving IoT devices (such as an industrial environment). This may include optimization of benefits (such as relating to safety, satisfaction, health), and others); (¶ [0077]: Controller is further configured to train an artificial intelligence (AI) model based on industry-specific feedback relating to the industrial environment); (¶ [4120]: digital twin simulation system may recursively simulate impacts of the one or more states until achieving a desired fit, provide the simulated values to the cognitive intelligence system for evaluation and determination of potential actions, receive the potential actions, evaluate impacts of each of the potential actions for a respective desired fit (e.g., optimizing system, worker, user, or personal safety, etc.).
Claim 5: Cella teaches the limitations of claim 1. Furthermore, Cella teaches the following:
Wherein the at least one predicted hydrogen production operation indicator comprises a predicted hydrogen production cost indicator; (¶[1885]: systems related to hydrogen production, storage, distribution and use may include, be associated with, or integrate improvement features. Improvement features may include process control and heat recovery, flow control and precision control, safety, and process and output quality including output consistency); (¶ [4120]: digital twin system employs the digital twin simulation system to simulate one or more impacts, such as immediate, upstream, downstream, and/or continuing effects, of recognized states. The digital twin simulation system may recursively simulate impacts of the one or more states until achieving a desired fit, provide the simulated values to the cognitive intelligence system for evaluation and determination of potential actions, receive the potential actions, evaluate impacts of each of the potential actions for a respective desired fit (e.g., cost functions for minimizing production disturbance, optimizing system, worker, user, or personal safety, etc.).
Wherein the one or more hydrogen production machine learning models comprise a hydrogen production cost prediction machine learning model. (¶ [4120] see above); (¶ [0409]: a platform is provided having training AI models based on industry-specific feedback. The various embodiments of cognitive systems disclosed herein may take inputs and feedback from industry-specific and domain-specific sources. Thus, learning and adaptation of storage organization, network usage, combination of sensor and input data, data pooling, data packaging, data pricing, and other features may be configured by learning on the domain-specific feedback measures of a given environment or application, such as an application involving IoT devices (such as an industrial environment). This may include optimization of performance measures (such as returns on investment, yields, profits, margins, revenues and the like), and reduction of costs (including labor costs, bandwidth costs, data costs, material input costs, licensing costs, and many others)); (¶ [0077]: Controller is further configured to train an artificial intelligence (AI) model based on industry-specific feedback relating to the industrial environment).
Claim 6: Cella teaches the limitations of claim 5. Furthermore, Cella teaches the following:
Receive a plurality of historical hydrogen production variable indicators associated with the hydrogen production facility; (¶ [0394]: systems are disclosed herein for cloud-based, machine pattern analysis of state information from multiple industrial sensors to provide anticipated state information for an industrial system. Machine learning may take advantage of a state machine, such as tracking states of multiple analog and/or digital sensors, feeding the states into a pattern analysis facility, and determining anticipated states of the industrial system based on historical data about sequences of state information).
Receive a plurality of historical hydrogen production cost indicators associated with the hydrogen production facility; and (¶ [0394]: see above); (¶[1028]: Intelligent management of data collection via smart bands may improve various parameters of data collection, such as yield parameters (including financial yield, output yield, and reduction of adverse events)); (¶ [1060]: vibration noise may be used by the expert system to confirm the status of a machine, such as a favorable operation, a production rate, a financial efficiency (e.g., output per cost), and the like. The expert system may make a comparison of the vibration noise with a stored vibration fingerprint).
Train the hydrogen production cost prediction machine learning model based at least in part on the plurality of historical hydrogen production variable indicators and the plurality of historical hydrogen production cost indicators. (¶ [00394]: see above); (¶ [1028]: see above); (¶ [1569]: see above); (¶ [0409]: a platform is provided having training AI models based on industry-specific feedback. The various embodiments of cognitive systems disclosed herein may take inputs and feedback from industry-specific and domain-specific sources. Thus, learning and adaptation of storage organization, network usage, combination of sensor and input data, data pooling, data packaging, data pricing, and other features may be configured by learning on the domain-specific feedback measures of a given environment or application, such as an application involving IoT devices (such as an industrial environment). This may include optimization of performance measures (such as returns on investment, yields, profits, margins, revenues and the like), and reduction of costs (including labor costs, bandwidth costs, data costs, material input costs, licensing costs, and many others)); (¶ [4120]: The digital twin simulation system may recursively simulate impacts of the one or more states until achieving a desired fit, provide the simulated values to the cognitive intelligence system for evaluation and determination of potential actions, receive the potential actions, evaluate impacts of each of the potential actions for a respective desired fit (e.g., cost functions for minimizing production disturbance, optimizing system, worker, user, or personal safety, etc.); (¶ [0077]: Controller is further configured to train an artificial intelligence (AI) model based on industry-specific feedback relating to the industrial environment).
Claim 7: Cella teaches the limitations of claim 1. Furthermore, Cella teaches the following:
Wherein the plurality of runtime hydrogen production variable indicators comprises the runtime hydrogen production variable indicator and one or more additional runtime hydrogen production variable indicators. (¶ [0394]: Methods and systems are disclosed herein for cloud-based, machine pattern analysis of state information from multiple industrial sensors to provide anticipated state information for an industrial system. Machine learning may take advantage of a state machine, such as tracking states of multiple analog and/or digital sensors, and determining anticipated states of the industrial system based on historical data. A wide range of measurement values and anticipated states may be managed by a state machine, relating to temperature, pressure, vibration, acceleration, momentum, inertia, friction, heat, heat flux, galvanic states, magnetic field states, electrical field states, capacitance states, charge and discharge states, motion, position, and many others).
Claim 8: Cella teaches the limitations of claim 7. Furthermore, Cella teaches the following:
Wherein, prior to transmitting the adjusted hydrogen production variable indicator, the at least one non-transitory memory and the computer program code are configured to, with the at least one processor, cause the apparatus to: generate a predicted hydrogen production operation indicator based at least in part on inputting the adjusted hydrogen production variable indicator and the one or more additional runtime hydrogen production variable indicators to the one or more hydrogen production machine learning models; (¶ [0394]: see above); (¶ [4120]: The digital twin simulation system may recursively simulate impacts of the one or more states until achieving a desired fit, provide the simulated values to the cognitive intelligence system for evaluation and determination of potential actions, receive the potential actions, evaluate impacts of each of the potential actions for a respective desired fit (e.g., cost functions for minimizing production disturbance, optimizing system, worker, user, or personal safety, etc.).
Determine whether the predicted hydrogen production operation indicator satisfies the at least one corresponding hydrogen production operation threshold indicator; (¶ [4120]: see above)); (¶ [4121]: the digital twin simulation system and the cognitive intelligence system may repeatedly share and update the simulated values and response actions for each desired outcome until desired conditions are met (e.g., convergence for each evaluated cost function for each evaluated action)).
In response to determining that the predicted hydrogen production operation indicator satisfies the at lest one corresponding hydrogen production operation threshold indicator, transmit the adjusted hydrogen production variable indicator to the hydrogen production control system. (¶ [4120]-¶ [4121]: see above); ([4127]: mitigating actions may include, for example, stopping power-consuming elements within the environment, reducing power supplied to one or more devices within the environment, providing power from an alternative power source external to the environment, allocating power from power storage devices within the environment, combinations thereof, and the like); (¶ [1793]: control module may control one or more aspects of an industrial setting based on a determination made by the AI system. The control module may be configured to provide commands to a device or system at the industrial setting to take a remedial action in response to a particular issue being detected); (¶[1708]: the edge system may include, link or connect to, integrate with, or be integrated into a control system, such as for providing control for one or more industrial entities, such as controlling a machine in a factory, controlling a workflow, or controlling sub-systems, systems, or operations of an entire factory or set of factories).
Claim 9: The limitations of claim 9 are substantially similar to the limitations of claim 1. Accordingly, claim 9 is rejected for the same reasons and rationale as set forth above with regard to claim 1.
Claim 10: Cella teaches the limitations of claim 9. Furthermore, the limitations of claim 10 are substantially similar to the limitations of claim 4. Accordingly, claim 10 is rejected for the same reasons and rationale as set forth above with regard to claim 4.
Claim 11: Cella teaches the limitations of claim 9. Furthermore, the limitations of claim 10 are substantially similar to the limitations of claim 2. Accordingly, claim 11 is rejected for the same reasons and rationale as set forth above with regard to claim 2.
Claim 12: Cella teaches the limitations of claim 9. Furthermore, the limitations of claim 10 are substantially similar to the limitations of claim 5. Accordingly, claim 12 is rejected for the same reasons and rationale as set forth above with regard to claim 5.
Claim 13: Cella teaches the limitations of claim 12. Furthermore, the limitations of claim 10 are substantially similar to the limitations of claim 6. Accordingly, claim 13 is rejected for the same reasons and rationale as set forth above with regard to claim 6.
Claim 14: Cella teaches the limitations of claim 9. Furthermore, the limitations of claim 10 are substantially similar to the limitations of claim 7. Accordingly, claim 14 is rejected for the same reasons and rationale as set forth above with regard to claim 7.
Claim 15: Cella teaches the limitations of claim 14. Furthermore, the limitations of claim 10 are substantially similar to the limitations of claim 8. Accordingly, claim 15 is rejected for the same reasons and rationale as set forth above with regard to claim 8.
Claim 16: The limitations of claim 16 are substantially similar to the limitations of claim 1. Accordingly, claim 16 is rejected for the same reasons and rationale as set forth above with regard to claim 1.
Claim 17: Cella teaches the limitations of claim 16. Furthermore, the limitations of claim 17 are substantially similar to the limitations of claim 3. Accordingly, claim 17 is rejected for the same reasons and rationale as set forth above with regard to claim 3.
Claim 18: Cella teaches the limitations of claim 17. Furthermore, the limitations of claim 18 are substantially similar to the limitations of claim 4. Accordingly, claim 18 is rejected for the same reasons and rationale as set forth above with regard to claim 4.
Claim 19: Cella teaches the limitations of claim 16. Furthermore, the limitations of claim 19 are substantially similar to the limitations of claim 5. Accordingly, claim 19 is rejected for the same reasons and rationale as set forth above with regard to claim 5.
Claim 20: Cella teaches the limitations of claim 19. Furthermore, the limitations of claim 20 are substantially similar to the limitations of claim 6. Accordingly, claim 20 is rejected for the same reasons and rationale as set forth above with regard to claim 6.
Conclusion
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/JORGE G DEL TORO-ORTEGA/Examiner, Art Unit 3628
/JEFF ZIMMERMAN/Supervisory Patent Examiner, Art Unit 3628
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