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 . In an amendment filed on 03/30/2026, claims 21-43 were added as new claims, and claims 1-20 were amended. Therefore, claims 1-43 are pending in this Application.
Response to Amendments/Remarks
Applicant’s arguments on pages 16-19, with respect to rejections to claims 1-43 under 35 USC § 102 have been fully considered and are persuasive. Therefore, rejections to the claims under 35 USC § 102 have been withdrawn.
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.
Claims 1-43 are 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 1, respectively, recite “the AI-based platform comprising: wherein the delivery is based on…a provisioning of energy resources for the delivery of the heterogeneous set of energy types, wherein the energy resources provisioned for delivering each energy type of the heterogeneous set of energy types are based on each energy type” and claim 32 “system comprising a provisioning of energy resources for the delivery of the heterogeneous set of energy types, wherein the energy resources provisioned for delivering each energy type of the heterogeneous set of energy types are based on each energy type”. These limitations are unclear and confusing.
The Applicant suggested paragraph [0038], [0060], [0068, [0091]… for support for the new amendments, however, none of the cited paragraphs or the disclosure as whole relates the claimed subject matter to the disclosure explicitly.
The disclosure [0038] recites “For example, the energy delivery orchestration systems 147 may enable orchestration of the delivery of energy to a point of consumption, such as by fixed transmission lines, wireless energy transmission, delivery of fuel, delivery of stored energy (e.g., chemical or nuclear batteries), or the like, and may involve autonomously optimizing the mix of energy types among the foregoing available resources based on various factors, such as location (e.g., based on distance from the grid), purpose or type of consumption (e.g., whether there is a need for very high peak energy delivery, such as for power-intensive production processes), and the like”.
The term “a provisioning” is unclear if it is a step or function performed by the platform or the system, this term is not defined in the disclosure. The term “energy resources” when interpreted as a DER (distributed energy resources in [0038]), then, the whole limitation is confusing. For instance replacing DER in one interpretation “, “a provisioning energy resources (DERS),…wherein the DERs (energy resources) provisioned for delivering each energy type of the heterogeneous set of energy types are based on each energy type”. Thus, the limitations “wherein the energy resources provisioned for delivering each energy type of the heterogeneous set of energy types are based on each energy type”, are unclear and confusing. This is understood as “The energy resources provisioned (provided or supplied) are based on energy type”. However, the limitations does not make sense. It seems that the this limitations is missing some terms at the end of limitation. This can be interpreted as “the energy resources provided are based on a type energy generated by these energy resources, a type of need, ” (as suggested in [0084] “type of consumption, type of generation”, [0125] “type of needs”)
For purposes for Examination, and in light of the cited paragraphs, the limitations above will be interpreted as:
“wherein the delivery is based on,… provisioned energy resources for the delivery of the heterogeneous set of energy types, wherein the energy resources provisioned for delivering each energy type of the heterogeneous set of energy types are based on each energy type that can be used at the point of consumption or is available or is generated” (as supported in the Abstract and [0038] or the original disclosure).
Claim 32 further recites “…a data center associated with a point of consumption of energy, wherein the data center processes at least one workload associated with the consumption of energy…”. The term “the consumption of energy” lacks of proper antecedent basis in the claim. It is unclear if this term refers back to “a point of consumption of energy”.
For purposes of Examination, claim 32 will be interpreted as:
“32…a data center associated with a point of consumption of energy, wherein the data center processes at least one workload associated with a consumption of energy…”
As to claim 2-31 and 33-43, these claims depend on claim 22 and 32 respectively, thus, they are rejected for the same reasons mutatis mutandis as their parent claim since they inherit the same error.
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 22-27, 29-31, 34-39 and 41-43 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per claim 22 recites “the delivery of at least a portion of the heterogeneous set of energy types to the point of consumption is based on a timing of energy demand at the point of consumption, the timing is determined by an energy provisioning and governance solution associated with the data center based on a priority of a workload of the data center”. These limitations do not have sufficient support in the disclosure as originally filed. The Applicant did not provide specific support for the amendments as required in the MPEP 714.02 and/or 2163.06 “Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06”.
As best understood, these limitations requires the determination or calculation of a specific time/timing of energy demand to deliver energy to a point of consumption, wherein the timing is determined by an application 156 called “the energy provision and governance solutions” in Fig. 2A. This application is briefly described in the disclosure paragraphs [0072] which recites “…The energy provisioning and governance solutions 156 may include solutions for governance of mining operations. Cobalt, nickel, and other metals are fundamental components of the batteries that will be needed for the green EV revolution,…; [0073], and each of [0103-[0107]. The term workload appears in [0260] and original claim 19 and is not associated with the data center as point of consumption. The term priority of a workload appears in the disclosure in [0260] “…In some embodiments, the quantum database engine may have an embedded policy engine 3830 for prioritization and/or allocation of quantum workflows, including prioritization of query workloads, such as based on overall priority as well as the comparative advantage of using quantum computing resources versus others…”. However, this is not related to a data center and to the application 156 as claimed.
Therefore, the disclosure does not have sufficient support for “the timing is determined by an energy provisioning and governance solution associated with the data center based on a priority of a workload of the data center”.
Note: the Applicant is welcome to provide support for each limitation individually by pointing specifically support in the disclosure (see MPEP 2163.06., e.g. for each limitation, please provide the specific paragraph and lines where the support for the amended subject matter is assumed).
As per claim 23 recites “the timing is further based on a delivery of data to the data center by an adaptive energy data pipeline, and the workload includes processing the data delivered by the adaptive energy data pipeline to the data center. These limitations do not have sufficient support in the disclosure as originally filed.
According to claim 22, the timing of the energy demand is based on a priority of a workload. This suggests that the timing is a future time or future period, which suggests future energy demand and future workload. Claim 23 recites that the timing is based on a delivery of data by an adaptive energy data pipeline (interpreted as network of communication and an application). This suggests that the timing of energy demand is based on data being delivered. This contradicts the limitation of claim 22. However, in any case, the disclosure does not have sufficient support for the claimed limitations of claim 23 such as “the timing is further based on a delivery of data to the data center by an adaptive energy data pipeline”.
The adaptive energy data pipeline is described in [0048] as an application that manages or handles data processing, filtering, compression, storage, routing, transport, error correction, security, extraction, transformation, loading, normalization, cleansing and/or other data handling capabilities involved in the transport of data over a network or communication system. It is also described in [0086], and [0087]. However, none of these paragraphs have sufficient support for “the timing is further based on a delivery of data to the data center by an adaptive energy data pipeline” and “and the workload includes processing the data delivered by the adaptive energy data pipeline to the data center” when combined with claim 22.
For purposes of Examination and/or interpretation, claim 23 will be interpreted as:
Claim 23 recites “the timing of energy demand is further based on data to be delivered data to the data center by an adaptive energy data pipeline, and the workload includes processing the data to be delivered by the adaptive energy data pipeline to the data center.
As per claim 24 recites “wherein the delivery of the data to the data center by the adaptive energy data pipeline is based on a timing of the delivery of at least a portion of the heterogeneous set of energy types to the point of consumption associated with the data center”. These limitations do not have sufficient support in the disclosure as originally filed.
As per claim 25, recites “the delivery of the data to the data center by the adaptive energy data pipeline is adapted to determine at least one property of the delivery of the data to the data center based on the timing of the delivery of at least a portion of the heterogeneous set of energy types to the point of consumption associated with the data center, and
the at least one property includes at least one of,
a volume of the data delivered to the data center by the adaptive energy data pipeline, a filtering of the data delivered to the data center by the adaptive energy data pipeline, a timing of the data delivered to the data center by the adaptive energy data pipeline, or a priority of the data delivered to the data center by the adaptive energy data pipeline”. These limitations do not have sufficient support in the disclosure as originally filed.
The disclosure does not have support for “the delivery of the data…is adapted to determine at least one property of the delivery of the data to the data center based on the timing of the delivery of at least a portion of the heterogeneous set of energy types including at least one of volume, a filtering, or a priority of data delivered”. Paragraph [0086] recites “In embodiments, the adaptive energy data pipeline 164 may include self-organizing data storage 412 (such as storing data on a device or system (e.g., an edge, IoT, or other networking device, cloud or data center system, on-premises system, or the like) based on the patterns or attributes of the data (e.g., patterns in volume of data over time, or other metrics). This does not have support for “the delivery of the data…is adapted to determine at least one property of the delivery of the data to the data center… the at least one property includes at least one of, including at least one of volume, filtering…. based on the timing of the delivery of energy.”. The disclosure does not have any instance of “determining a property of data being delivered such as volume of data being determined or calculated based on the timing of the delivery of energy. The disclosure does not have any instance of a determining a property such as a “filtering“ of data to delivered to the data center based on the timing of the delivery of energy. Filtering does not seem to be a property of data. The disclosure does not have any instance of “determining a property of data being delivered such as priority of the data delivered being determined or calculated based on the timing of the delivery of energy.
As per claim 26 recites “the workload of the data center is based on a determination of a security event, and the timing of energy demand at the point of consumption is based on a workload of the data center in response to the security event”. These limitations do not have sufficient support in the disclosure as originally filed. The only instance where “security event” is recited in the disclosure is in paragraph [0315]. The term “workload” is recited in [0260] and in original claim 19. These two paragraphs do not have support for the claimed subject matter of claim 26.
As per claim 27 recites “The AI-based platform of claim 26, wherein, the security event is based on at least one unusual pattern associated with a change of state, and
the timing of the energy demand is based on a determination of a priority of the security event based on an analysis of the at least one unusual pattern”. These limitations do not have sufficient support in the disclosure as originally filed. The only instance where “security event” is recited in the disclosure is in paragraphs [0315]-[0316]. The term “workload” is recited in [0260] and in original claim 19. These two paragraphs does not have support for the claimed subject matter of claim 27.
As to claims 23-27, these claims depend on claim 22, and they are rejected for the same reasons mutatis mutandis as their parent claim since they inherit the same error.
As per claim 29 recites The AI-based platform of claim 1, ”wherein, the point of consumption is mobile”. These limitations do not have sufficient support in the disclosure as originally filed. The disclosure suggest that some of the energy providing assets are mobile such as vehicles, batteries, etc. However, the disclosure does not suggests a mobile point of consumption.
As to claim 30-31, these claims depend on claim 29, and they are rejected for the same reasons mutatis mutandis as their parent claim since they inherit the same error.
As per claim 34, recites “wherein, the data center includes an energy provisioning and governance solution that determines,
a priority of a workload of the data center, and
a timing of energy demand based on the priority of the workload, and
the delivery of at least a portion of the heterogeneous set of energy types to the point of consumption is based on a timing of energy demand at the point of consumption”. These limitations do not have sufficient support in the disclosure as originally filed..
As best understood, these limitations requires the determination or calculation of a specific time/timing of energy demand to deliver energy to a point of consumption, wherein the timing is determined by an application 156 called “the energy provision and governance solutions” in Fig. 2A. This application is briefly described in the disclosure paragraphs [0072] “…The energy provisioning and governance solutions 156 may include solutions for governance of mining operations. Cobalt, nickel, and other metals are fundamental components of the batteries that will be needed for the green EV revolution,…; [0073], and each of [0103-[0107]. The term workload appears in [0260] and original claim 19 and is not associated with the data center as point of consumption. The term priority of a workload appears in the disclosure in [0260] “…In some embodiments, the quantum database engine may have an embedded policy engine 3830 for prioritization and/or allocation of quantum workflows, including prioritization of query workloads, such as based on overall priority as well as the comparative advantage of using quantum computing resources versus others…”. However, this is not related to a data center and to the application 156.
Therefore, the disclosure does not have sufficient support for the claimed subject matter of claim 34 as recited.
As to claim 35-39, these claims depend on claim 34, and they are rejected for the same reasons mutatis mutandis as their parent claim since they inherit the same error.
As per claim 35 further recites “the data center processes a workload based on data delivered to the data center by an adaptive energy data pipeline, and
the timing is further based on the delivery of data to the data center by the adaptive energy data pipeline”. These limitations do not have sufficient support in the disclosure as originally filed.
According to claim 34, the timing of the energy demand is based on a priority of a workload. This suggests that the timing is a future time or future period, which suggests future energy demand and future workload. Claim 35 recites that the timing is based on a delivery of data by an adaptive energy data pipeline (interpreted as network of communication and an application). This suggests that the timing of energy demand is based on data being delivered. This contradicts the limitation of claim 34. However, in any case, the disclosure does not have sufficient support for the claimed limitations of claim 34 such as “the timing is further based on a delivery of data to the data center by an adaptive energy data pipeline”.
The adaptive energy data pipeline is described in [0048] as an application that manages or handles data processing, filtering, compression, storage, routing, transport, error correction, security, extraction, transformation, loading, normalization, cleansing and/or other data handling capabilities involved in the transport of data over a network or communication system. It is also described in [0086], and [0087]. However, none of these paragraphs have sufficient support for “the timing is further based on a delivery of data to the data center by an adaptive energy data pipeline” and “and the workload includes processing the data delivered by the adaptive energy data pipeline to the data center” when combined with claim 34.
For purposes of Examination and/or interpretation, claim 35 will be interpreted as:
Claim 35 recites “the data center processes a workload based on data to be delivered to the data center by an adaptive energy data pipeline, and the timing of energy demand is further based on data to be delivered data to the data center by an adaptive energy data pipeline”.
As per claim 36 further recites “wherein the delivery of the data to the data center by the adaptive energy data pipeline is based on a timing of the delivery of at least a portion of the heterogeneous set of energy types to the point of consumption associated with the data center”. These limitations do not have sufficient support in the disclosure as originally filed.
As per claim 37, further recites “wherein, the delivery of the data to the data center by the adaptive energy data pipeline is adapted to determine at least one property of the delivery of the data to the data center based on the timing of the delivery of at least a portion of the heterogeneous set of energy types to the point of consumption associated with the data center, and the at least one property includes at least one of,
a volume of the data delivered to the data center by the adaptive energy data pipeline,
a filtering of the data delivered to the data center by the adaptive energy data pipeline,
a timing of the data delivered to the data center by the adaptive energy data pipeline, or
a priority of the data delivered to the data center by the adaptive energy data pipeline” These limitations do not have sufficient support in the disclosure as originally filed (see claim 25 above same rationale applies herein).
As per claim 38 further recites “the workload of the data center is based on a determination of a security event, and the timing of energy demand at the point of consumption is based on a workload of the data center in response to the security event”. These limitations do not have sufficient support in the disclosure as originally filed. The only instance where “security event” is recited in the disclosure is in paragraph [0315]. The term “workload” is recited in [0260] and in original claim 19. These two paragraphs do not have support for the claimed subject matter of claim 38.
As per claim 39 further recites “the security event is based on at least one unusual pattern associated with a change of state, and the timing of the energy demand is based on a determination of a priority of the security event based on an analysis of the at least one unusual pattern”. These limitations do not have sufficient support in the disclosure as originally filed. The only instance where “security event” is recited in the disclosure is in paragraphs [0315]-[0316]. The term “workload” is recited in [0260] and in original claim 19. These two paragraphs does not have support for the claimed subject matter of claim 39.
As per claim 41 further recites ”wherein, the point of consumption is mobile”. These limitations do not have sufficient support in the disclosure as originally filed. The disclosure suggest that some of the energy providing assets are mobile such as vehicles, batteries, etc. However, the disclosure does not suggests a mobile point of consumption.
As to claim 42-43, these claims depend on claim 41, and they are rejected for the same reasons mutatis mutandis as their parent claim since they inherit the same error.
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) 1-6, 8, 12, 15-16, 18-19, 21, 28, 32-33, 40 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al (US 20190033845 cited in the IDS) in view of Li et al (US 9218035).
As per claim 1, Cella teaches an artificial-intelligence-based (AI-based) (AI-based) platform for enabling intelligent orchestration and management of power and energy (see Fig. 6 and see [0216] and [0219] platform 100; and see [0264], [0312]-[0316], [0326] “…machine learning…”; also, see 0344] “.In embodiments, a platform is provided having training AI models based on industry-specific feedback … In embodiments, the various embodiments of cognitive systems disclosed herein may take inputs and feedback from industry-specific and domain-specific sources 116 (such as relating to optimization of specific machines, devices, components, processes, and the like)… This may include optimization of efficiency (such as in electrical, electromechanical, magnetic, physical, thermodynamic, chemical and other processes and systems), optimization of outputs (such as for production of energy, materials, products, services and other outputs) …” and see [0402]; also, see [1401]), the AI-based platform comprising:
a set of autonomous orchestration systems for improving delivery of a heterogeneous set of energy types to a point of consumption, wherein the delivery is based on (see [0216] “The platform 100 may include one or more local autonomous systems, such as for enabling autonomous behavior, such as reflecting artificial, or machine-based intelligence or such as enabling automated action based on the applications of a set of rules or models upon input data from the local data collection system 102 or from one or more input sources 116, which may comprise information feeds and inputs from a wide array of sources, including those in the local environment 104, in a network 110, in the host system 112, or in one or more external systems, databases, or the like” ;asp. See [0219]; also, see [0312 “the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from machines,…include turbines, generators…”; [0313]-[0315] “…In many embodiments, an array of steam turbines may be arranged and configured for high, medium, and low pressure, so they may optimally convert the respective steam pressure into rotational movement… The type of water turbine or hydro-power selected for a project may be based on the height of standing water, often referred to as head, and the flow (or volume of water) at the site. In this example, a generator may be placed at the top of a shaft that connects to the water turbine”; 0316 “In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from energy production environments, such as thermal, nuclear, geothermal, chemical, biomass, carbon-based fuels, hybrid-renewable energy plants, and the like. Many of these plants may use multiple forms of energy harvesting equipment like wind turbines, hydro turbines, and steam turbines powered by heat from nuclear, gas-fired, solar, and molten salt heat sources…”; [0326] “…. The learning machine may then operate on other data, initially using a set of rules or elements of a model, such as to provide a variety of outputs…such as recognition of certain patterns (…such as fuel efficiency, energy production, or the like)... For example, a model of fuel consumption by an industrial machine may include physical model parameters that characterize weights, motion, resistance, momentum, inertia, acceleration, and other factors that indicate consumption, and chemical model parameters (such as those that predict energy produced and/or consumed e.g., such as through combustion, through chemical reactions in battery charging and discharging, and the like). The model may be refined by feeding in data from sensors disposed in the environment of a machine, in the machine, and the like, as well as data indicating actual fuel consumption, so that the machine can provide increasingly accurate, sensor-based, estimates of fuel consumption and can also provide output that indicate what changes can be made to increase fuel consumption (such as changing operation parameters of the machine or changing other elements of the environment, such as the ambient temperature, the operation of a nearby machine, or the like). For example, if a resonance effect between two machines is adversely affecting one of them, the model may account for this and automatically provide an output that results in changing the operation of one of the machines (such as to reduce the resonance, to increase fuel efficiency of one or both machines); also, see also, see Fig. 164 and see [01399-01401]) a neural network to improve or optimize power efficiency/resource utilization. This embodiment is a neural network/ai based platform that accepts several attribute/parameters to optimize the output or delivery of power of a system such as power system; also, see [1411] “the targets 12002 can be any form of machinery or component thereof in an industrial environment 12000…target 12002…include computer equipment…”; also, see also, see Fig. 282 and 284 and [2130-2131] “…embodiments of the methods and systems related to renewable energy sources for hydrogen production, storage, distribution and use are depicted…Hydrogen production, storage, distribution, and use may be at least partially powered by one or more renewable energy sources, such as solar energy source 5709, wind energy source 5711, hydro energy source 5713, geothermal energy source 5715, and the like…”; also, see [2122-2137], [2150-2157], [2162] “…When a renewable energy source is available, yet hydrogen production is not called for (e.g., sufficient supply is stored, or an amount that is anticipated to be needed, such as based on machine learning or the like of prior local hydrogen demand over time is expected to be producible before needed), then electricity or the like produced from the renewable energy source could be fed back into the smart grid…”, thus, the delivery of energy is improved),
a location of the point of consumption (see [01399] “ The self-organization functionality, in embodiments involving a neural net or other machine learning system…location of power sources..”; also, see Fig. 164 and see [01400-01401]; also, see Fig. 282 and [2122-2137], [2150-2157] [2162], emphasis in [2151] “…Likewise, sources of energy for operating a hydrolyzer and the like as described herein, such as renewable energy from solar and wind may be managed so that available sunlight and/or the wind may be tied to hydrogen production demand predictions from users such as industrial and others. In embodiments, this may facilitate ensuring allocation of available hydrogen for grid stability and the like. In embodiments, sensors that measure integrated energy use may similarly provide information to further facilitate managing for grid stability, among other things. In examples, predicted demand may be used in determining when and how much hydrogen should be produced and whether it should be stored to facilitate grid stability. In embodiments, this information may be used when portions of a grid are predicted to have high demand, while other portions are predicted to have low demand…”, portions of a grid suggests a location on the grid),
a set of consumption attributes including at least one of (this requires only one of the following attributes since it is in the alternative):
a peak power requirement at the point of consumption (see [01117] “…peak energy consumption…”; also, see Fig. 164 and see [01400-01401]; also, see Fig. 282 and [2122-2137], [2150-2157] [2162], emphasis in [2151] “…Likewise, sources of energy for operating a hydrolyzer and the like as described herein, such as renewable energy from solar and wind may be managed so that available sunlight and/or the wind may be tied to hydrogen production demand predictions from users such as industrial and others. In embodiments, this may facilitate ensuring allocation of available hydrogen for grid stability and the like. In embodiments, sensors that measure integrated energy use may similarly provide information to further facilitate managing for grid stability, among other things. In examples, predicted demand may be used in determining when and how much hydrogen should be produced and whether it should be stored to facilitate grid stability. In embodiments, this information may be used when portions of a grid are predicted to have high demand, while other portions are predicted to have low demand…”, high demands suggests peak power at certain point of consumption);
a continuity of power requirement at the point of consumption (see [01399] …power availability…”; see [2151] “…Likewise, sources of energy for operating a hydrolyzer and the like as described herein, such as renewable energy from solar and wind may be managed so that available sunlight and/or the wind may be tied to hydrogen production demand predictions from users such as industrial and others; also, see [2162] “…may be deployed as components in a smart power grid that may operate cooperatively with other components of a smart grid to attempt to deliver reliable energy available throughout the grid…When a renewable energy source is available, yet hydrogen production is not called for (e.g., sufficient supply is stored, or an amount that is anticipated to be needed, such as based on machine learning or the like of prior local hydrogen demand over time is expected to be producible before needed), then electricity or the like produced from the renewable energy source could be fed back into the smart grid”; also, see [2182] “…Energy sources that may be included in such an automated selection process may include solar energy, wind energy, hydrogen energy, sulfur dioxide, electricity (such as from an electricity grid), natural gas, and the like. In embodiments, an algorithm that may facilitate automatic energy selection may receive information about each energy source, such as availability,… to determine which energy source provides the best fit for operating the hydrolyzer in a given time period. By way of this example, the algorithm may favor energy sources that are more reliable, more available, and lower costs than those that are less reliable, less available, and costlier. In embodiments, combinations of these three factors may result in certain sources being selected. If a demand for reliable energy at a particular time is weighted more highly than price, for example, a costlier energy source may be automatically selected due to it being more reliably available”); and
a type of energy that can be used at the point of consumption (see [01401] “…type of equipment…”; also, see 2181 “..Example factors may include the price of other energy sources, including energy sources that are available to the cooking and heating system as well as those that are not directly available. In this way, selecting an energy source may be driven by other considerations, such as which energy source is better for the environment, and the like. In embodiments, an automatic energy source selection may be based, at least in part on the anticipated availability of an energy source…”), and
a provisioning of energy resources for the delivery of the heterogeneous set of energy types (see Fig. 282-283 the energy delivered to the load 5703 from a set of different energy resources for the delivery of the heterogeneous/diverse set of energy types; also, see [0278] “…The selected source of renewable energy may be based on characteristics of the environment; for example, marine industrial environments may have available wind and hydro-power, agricultural environments may have solar power, etc.…”; also, see [2151] “…sources of energy for operating a hydrolyzer and the like as described herein, such as renewable energy from solar and wind may be managed so that available…”; also, see [2157], [2162], ), wherein the energy resources provisioned for delivering each energy type of the heterogeneous set of energy types are based on each energy type (this has been interpreted in the broadest reasonable interpretation in light of the disclosure as “wherein the energy resources provisioned for delivering each energy type of the heterogeneous set of energy types are based on each energy type that can be used at the point of consumption, each energy type that available/availability, or power generated”; see Fig. 282-283 and see [01401] “…type of equipment…”; also, see [2181] “..Example factors may include the price of other energy sources, including energy sources that are available to the cooking and heating system as well as those that are not directly available. In this way, selecting an energy source may be driven by other considerations, such as which energy source is better for the environment, and the like. In embodiments, an automatic energy source selection may be based, at least in part on the anticipated availability of an energy source…”).
While Cella teaches the systems are associated with a data center (see [1411]), and the platform is for the optimization of energy control/delivery to a load (see Figs. 164 and [1411] power generation environments; also, see Figs. 282-283 and see [2122-2134], [2185]), Cella does not explicitly teach the point of consumption associated with a data center.
Li teaches an energy delivery optimization system comprising improving delivery heterogeneous of energy types to a point of consumption associated with a data center (see Fig. 8; see Col 4 lines 17-35 “… system level embodiments of renewable energy control systems (also referred to herein also as iSwitch) that address the operational overhead of a renewable energy powered data center…”; see Col 5 lines 5-10 “…Specifically, certain embodiments of SolarCore provide a joint optimization of green energy utilization and workload performance for multi-core processors, which comprise the mainstream hardware design choice for today's IT industries and demand increasing amounts of power to unleash their full computation potential…”; see Col 9 lines 48-50 “Certain optimization methods can be employed to maximize workload performance under the variable maximal power budget.”; also, see Col 11 lines 54 to Col 12 line 65; also, see Col 19 lines 49-61 “… In the description that follows, a data center design scheme is disclosed that integrates on-site renewable energy sources into the data center infrastructure…”; also, see Col 20 lines 20-23 “Certain embodiments of iSwitch provide a power management scheme that maintains a desirable balance between renewable energy utilization and data center performance.” And lines 32-52 “…iSwitch comprise a control architecture that provides an application-independent hierarchical control that leverages load migration to best utilize the renewable energy generation. Characterization of renewable power variability and data center load fluctuation reveals that power tracking may be done in a less frequent,…”; also, see Fig. 26 and Col 23 lines 23-38).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella’s invention to include improving delivery heterogeneous of energy to a point of consumption associated with a data center as taught by Li in order to provide heterogeneous of energy types to a data center in an optimized manner according to the load demand of the data center which has a variable power consumption load (see Col 6 lines 20-32 “certain embodiments of SolarCore include a multi-core architecture power management scheme that addresses a two-fold challenge, namely to maximize a multi-core processor's total solar energy utilization by performing load matching under variable PV power output and to intelligently allocate the dynamically varied power budget across multiple cores so that the maximum workload performance can be achieved”; see Col 5 lines 5-10; see Col 9 lines 48-50 “Certain optimization methods can be employed to maximize workload performance under the variable maximal power budget.”; also, see Col 11 lines 54 to Col 12 line 65; also, see Col 19 lines 49-61 “… In the description that follows, a data center design scheme is disclosed that integrates on-site renewable energy sources into the data center infrastructure…”; also, see Col 20 lines 20-23 “Certain embodiments of iSwitch provide a power management scheme that maintains a desirable balance between renewable energy utilization and data center performance” and lines 32-52 “…iSwitch comprise a control architecture that provides an application-independent hierarchical control that leverages load migration to best utilize the renewable energy generation. Characterization of renewable power variability and data center load fluctuation reveals that power tracking may be done in a less frequent,…”; also, see Fig. 26 and Col 23 lines 23-38) and apply the AI platform of Cella to control the power sources in an optimized manner as well ( 0316 “In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from energy production environments, such as thermal, nuclear, geothermal, chemical, biomass, carbon-based fuels, hybrid-renewable energy plants, and the like. Many of these plants may use multiple forms of energy harvesting equipment like wind turbines, hydro turbines, and steam turbines powered by heat from nuclear, gas-fired, solar, and molten salt heat sources…”; see Fig. 164 and see [01399-01401]) a neural network to improve or optimize power efficiency/resource utilization. This embodiment is a neural network/ai based platform that accepts several attribute/parameters to optimize the output or delivery of power of a system such as power system; also, see [1411] “the targets 12002 can be any form of machinery or component thereof in an industrial environment 12000…target 12002…include computer equipment…”; also, see also, see Fig. 282 and 284 and [2130-2131] “…embodiments of the methods and systems related to renewable energy sources for hydrogen production, storage, distribution and use are depicted… Hydrogen production, storage, distribution, and use may be at least partially powered by one or more renewable energy sources, such as solar energy source 5709, wind energy source 5711, hydro energy source 5713, geothermal energy source 5715, and the like…”; also, see [2122-2137], [2150-2157], [2162] “…When a renewable energy source is available, yet hydrogen production is not called for (e.g., sufficient supply is stored, or an amount that is anticipated to be needed, such as based on machine learning or the like of prior local hydrogen demand over time is expected to be producible before needed), then electricity or the like produced from the renewable energy source could be fed back into the smart grid…”, thus, the delivery of energy is improved) .
As per claim 2, Cella-Li teaches the AI-based platform of claim 1, Cella
Further teaches wherein the set of autonomous orchestration systems orchestrates delivery of defined types of energy generation capacity to the point of consumption (see also, see [2182] “…Energy sources that may be included in such an automated selection process may include solar energy, wind energy, hydrogen energy, sulfur dioxide, electricity (such as from an electricity grid), natural gas, and the like. In embodiments, an algorithm that may facilitate automatic energy selection may receive information about each energy source, such as availability,… to determine which energy source provides the best fit for operating the hydrolyzer in a given time period. By way of this example, the algorithm may favor energy sources that are more reliable, more available, and lower costs than those that are less reliable, less available, and costlier. In embodiments, combinations of these three factors may result in certain sources being selected. If a demand for reliable energy at a particular time is weighted more highly than price, for example, a costlier energy source may be automatically selected due to it being more reliably available”; also, see [2185] “…for each type of energy source (solar, hydro-based, wind, exhaust gas, including sulfur dioxide use, and the like..”; also, see [01789] “… define at least one of an energy utilization policy, …”; also, see [2151], [2162]).
As per claim 3, Cella-Li teaches the AI-based platform of claim 1, Cella further teaches wherein the set of autonomous orchestration systems orchestrates delivery of defined types of energy storage capacity to the point of consumption (see [2122] “…the methods and systems disclosed herein may include, connect with or be integrated with hydrogen production, storage, and use systems. In embodiments, the hydrogen production, storage, and use systems may use renewable energy as a source of energy for various operations including hydrogen production, hydrogen storage…”; also, see [2125] “…Solar energy harvesting may also be used to charge a battery, charge various thermal systems, or other electrical energy storage facility that may directly provide the energy needed for hydrogen production immediately or with a time-shift and on-demand functions and other operational elements as described herein… the impact of an absence of sunlight and therefore diminished solar power production may be mitigated through the use of an intermediate battery or the like”; thus, batteries are coordinated to provided power when solar power is unavailable’ also, see [2187] “…methods and systems related to hydrogen production, storage, distribution and use…”; also, see [2150], [2151] “…In embodiments, this may facilitate ensuring allocation of available hydrogen for grid stability and the like… In embodiments, this may facilitate ensuring allocation of available hydrogen for grid stability and the like… Supply, from the production of hydrogen and/or from stored hydrogen, may be directed where when it is predicted to be needed or it is predicted to be needed in possibly relatively fewer quantities but may be consumed more quickly”; also, see [2157]; also, see [2162] “…in an example, a renewable energy-based hydrogen production system may utilize its renewable energy harvesting components to deliver electricity to a smart grid based on various factors, such as local demand for hydrogen and the like. When a renewable energy source is available, yet hydrogen production is not called for (e.g., sufficient supply is stored, or an amount that is anticipated to be needed, such as based on machine learning or the like of prior local hydrogen demand over time is expected to be producible before needed), then electricity or the like produced from the renewable energy source could be fed back into the smart grid.”, thus, stored energy from the stored hydrogen is used to provide power.
As per claim 4, Cella-Li teaches the AI-based platform of claim 1, Cella further teaches wherein the type of energy that can be used is determined at least in part based on a set of operational compatibility parameters (the term “operational compatibility parameters”, has not been defined in the disclosure and will be interpreted in the broadest reasonable interpretation (BRI) as operational data (0005), cost 90085, capacity (0085), time of operational use, operational conditions current or predicted (0095), operational needs or operational metrics (0096, 0115, 0116, 0119, 0126,), operational configuration of the event, ...market prices (0101); Cella teaches [2182] “Energy sources that may be included in such an automated selection process may include solar energy, wind energy, hydrogen energy, sulfur dioxide, electricity (such as from an electricity grid), natural gas, and the like. In embodiments, an algorithm that may facilitate automatic energy selection may receive information about each energy source, such as availability, costs, efficiency, and the like that may be processed by, for example comparing the information to determine which energy source provides the best fit for operating the hydrolyzer in a given time period. By way of this example, the algorithm may favor energy sources that are more reliable, more available, and lower costs than those that are less reliable, less available, and costlier. In embodiments, combinations of these three factors may result in certain sources being selected. If a demand for reliable energy at a particular time is weighted more highly than price, for example, a costlier energy source may be automatically selected due to it being more reliably available”, Thus, the type of energy that can be used is determined at least in part based on a set of operational compatibility parameters such as cost, reliability, availability, efficiency).
As per claim 5, Cella-Li teaches the AI-based platform of claim 1, Cella further teaches wherein the type of energy that can be used is determined at least in part based on a set of governance parameters (the term “governance parameters” has not been explicitly defined in the disclosure and is related to the use of renewable resources or carbon generation/emissions according to claims 6-7 below. Therefore, it will be interpreted in the BRI in light of the disclosure as the type of energy that can be used is related to the use of renewable resources, carbon generation or emission, pollution (0093), etc., government policies or rules, track carbon generation or emissions (0103-0104); Cella further teaches [2182] “..,The set of futures market optimization systems 724 may automatically orchestrate aggregation of a set of futures markets contracts for energy, renewable energy credits, for carbon offsets or abatement credits, for pollution abatement credits, or the like based on a forecast of future energy needs for an individual or enterprise ).
As per claim 6, Cella-Li teaches the AI-based platform of claim 5, Cella further teaches wherein the set of governance parameters relates to use of renewable energy resources (see [2125] “…Solar energy harvesting may also be used to charge a battery, charge various thermal systems, or other electrical energy storage facility that may directly provide the energy needed for hydrogen production immediately or with a time-shift and on-demand functions and other operational elements as described herein…”; also, see [2130-2131] “…solar energy source 5709, wind energy source 5711, hydro energy source 5713, geothermal energy source 5715, and the like. A wind energy source 5711 may be natural air currents, motor driven air currents, air currents resulting from movement of a vehicle, or waste air flow sources 5719 (such as waste heat from heating operations, such as cooking and the like). Any of these renewable energy sources may be converted into a form of energy that is suitable for an intended use by the hydrogen production, storage, distribution, and use system….”; also, see [2157], [2182] “…,The set of futures market optimization systems 724 may automatically orchestrate aggregation of a set of futures markets contracts for energy, renewable energy credits, for carbon offsets or abatement credits, for pollution abatement credits, or the like based on a forecast of future energy needs for an individual or enterprise…”, [2183]).
As per claim 8, Cella-Li teaches the AI-based platform of claim 1, Cella further teaches wherein at least one of the set of autonomous orchestration systems is configured to adapt a transport of data over a network and/or communication system (see [0018] “multi-sensor data collector that can optimize based on bandwidth, quality of service, pricing, and/or other network conditions”; also, see [0353] “…Thus, a self-organizing, network-condition-adaptive data collection system is provided….”; [0481], [0610], [1889], [2664]), wherein the adapting is based on at least one of (only one parameter/factor below is required),
a congestion condition ([02025] “individual congestion control loops may be employed on each path to adapt to the available bandwidth and congestion on the path…”),
a delay condition ([1887]),
latency condition ([1887] “..adapting to changes in average throughput, latency, etc.”),
a packet loss condition ([1008] “machine learning system… Certain feedback may include utilization measures…data loss…”; [1954], [1964], [1969-1971], [2015]),
an error rate condition ([1008] “machine learning system… Certain feedback may include utilization measures…error rate in transmission” ),
a cost of transport condition ([1520] “…. An example transmission condition 12254 includes a change in a cost of transmitting information 12298 (e.g., cost has increased or decreased, where cost may be a direct cost parameter such as a data transmission subscription cost…”; [1582]),
a quality-of-service (QoS) condition (see [0018], [1530], [1592],[1594], [1670], [2213] “a network condition-sensitive, self-organizing, multi-sensor data collector that can optimize based on bandwidth, quality of service…”, [2340]),
a usage condition,
a market factor condition, or
a user configuration condition.
As per claim 12, Cella-Li teaches the AI-based platform of claim 1, Cella further teaches wherein at least one of the set of autonomous orchestration systems is configured to perform at least one of (only one function/factor below is required to read in the claims),
extracting energy-related data ([2238], [2792]),
detecting errors in energy-related data ([1673], [1674], [1864], [1895]),
correcting errors in energy related data ([1673], [1674], [1864], [1895]),
transforming energy-related data (0324),
converting energy-related data (see 0324),
normalizing energy-related data (1083),
cleansing energy-related data (1083 and [1396]),
parsing energy-related data ([0392] “…parse…”),
detecting patterns in energy-related data (02191),
detecting content in energy-related data (0014),
detecting objects in energy-related data (see [0363] “…training a model of the machine learning facility to recognize a defined pattern associated with the industrial environment. Embodiments include using a cloud-based pattern recognizer on input states from a state machine that characterizes states of an industrial environment…”, [0365], [0390], and [2191] “…training a model of the machine learning facility to recognize a defined pattern associated with the industrial environment. Embodiments include using a cloud-based pattern recognizer on input states from a state machine that characterizes states of an industrial environment…”),
compressing energy-related data ([1518], [1529], [1533]),
streaming energy-related data (see [2238]),
filtering energy-related data ([0335], [0381], [1042]),
loading energy related data ([2191])
storing energy-related data ([2191]),
routing energy-related data (0023, 0037)
transporting energy-related data ([0346], [0465], [0481]), or
maintaining security of energy-related data (see [2187]).
As per claim 15, Cella-Li teaches the AI-based platform of claim 1, Cella further teaches further comprising at least one of an AI-based model or AI based algorithm, wherein the at least one AI-based model or algorithm is trained based on a training data set, and the training data set is based on at least one of (only one parameter/factor below is required),
at least one human tag or label (0218; 0964),
at least one human interaction with a hardware system (see 0218)
at least one human interaction with a software system (see 0218),
at least one outcome (0218, 0329, 1029),
at least one AI-generated training data sample,
a supervised learning training process (0324, 0935),
a semi-supervised learning training process, or
a deep learning training process (see [0324]; see [0343] “ Methods and systems are disclosed herein for training AI models based on industry-specific feedback, including training an AI model based on industry-specific feedback that reflects a measure of utilization, yield, or impact, and where the AI model operates on sensor data from an industrial environment….”; also, see [0935] “…a TDNN may be trained with supervised learning, such as where connection weights are trained with back propagation or under feedback…”; [0938] “unsupervised learning”).
As per claim 16, Cella-Li teaches the AI-based platform of claim 1, Cella further teaches wherein at least one of the set of autonomous orchestration systems is configured to orchestrate delivery of energy to at least one point of consumption, and the delivery of the energy includes at least one of (only one parameter/factor below is required),
at least one fixed transmission line (see Fig. 282 transmission lines to deliver power),
at least one instance of wireless energy transmission (see [01253] “…wireless recharging,),
at least one delivery of fuel (see [0283], [0326], also, see Fig. 282 and [2122-2137], [2150-2157] [2162], emphasis in [2151]), or
at least one delivery of stored energy (see [0326] “…. For example, a model of fuel consumption by an industrial machine…estimates of fuel consumption and can also provide output that indicate what changes can be made to increase fuel consumption (such as changing operation parameters of the machine or changing other elements of the environment, such as the ambient temperature, the operation of a nearby machine, or the like)”; also, see Fig. 282 and [2122-2137], [2150-2157] [2162], emphasis in [2151] “…Likewise, sources of energy for operating a hydrolyzer and the like as described herein, such as renewable energy from solar and wind may be managed so that available sunlight and/or the wind may be tied to hydrogen production demand predictions from users such as industrial and others. In embodiments, this may facilitate ensuring allocation of available hydrogen for grid stability and the like. In embodiments, sensors that measure integrated energy use may similarly provide information to further facilitate managing for grid stability, among other things. In examples, predicted demand may be used in determining when and how much hydrogen should be produced and whether it should be stored to facilitate grid stability. In embodiments, this information may be used when portions of a grid are predicted to have high demand, while other portions are predicted to have low demand…”, portions of a grid suggests a location on the grid and suggest fixed transmission lines; see [01241]).
As per claim 18, Cella-Li teaches the AI-based platform of claim 1, Cella further teaches wherein,
at least one of the set of autonomous orchestration systems is deployed in an off-grid environment, and the off-grid environment includes at least one of, an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system (see Fig. 282 and 284 and [2130-2131] “…embodiments of the methods and systems related to renewable energy sources for hydrogen production, storage, distribution and use are depicted… Hydrogen production, storage, distribution, and use may be at least partially powered by one or more renewable energy sources, such as solar energy source 5709, wind energy source 5711, hydro energy source 5713, geothermal energy source 5715, and the like…”; also, see [2122-2137], [2150-2157], [2162] “…When a renewable energy source is available, yet hydrogen production is not called for (e.g., sufficient supply is stored, or an amount that is anticipated to be needed, such as based on machine learning or the like of prior local hydrogen demand over time is expected to be producible before needed), then electricity or the like produced from the renewable energy source could be fed back into the smart grid…”, [2047] “…In embodiments, the intelligent cooking system may be fueled by a hydrogen generator, referred to herein in some cases as the electrolyzer, an independent fuel source that does not require traditional connections to the electrical power grid,”, e.g. off grid system; also, see [2125], [2151]; also, see [2043], [2047]). Li also teaches an orchestration system in an off-grid environment, and the off-grid environment includes at least one of, an off-grid energy generation system, an off-grid energy storage system, or an off-grid energy mobilization system (see Fig. 8 the system is disconnectable from the backup utility grid 806 (off-grid), with an off-grid energy generation system such as solar and storage system 812; see Col 8 lines 62-65) .
As per claim 19, Cella-Li teaches the AI-based platform of claim 1, Cella further teaches wherein the set of autonomous orchestration systems is configured to determine the delivery of the heterogeneous set of energy types based on a set of rules and/or policies that govern a set of at least one of energy generation, storage, or consumption workloads (see [0216] “The platform 100 may include one or more local autonomous systems, such as for enabling autonomous behavior, such as reflecting artificial, or machine-based intelligence or such as enabling automated action based on the applications of a set of rules or models upon input data from the local data collection system 102 or from one or more input sources 116…”; also, see [0331] “…The policy automation engine 4032 may apply the policies according to one or more models, such as based on the characteristics of a given device, machine, or environment. For example, a large machine, such as a machine for power generation, may include a policy that only a verifiably local controller can change certain parameters of the power generation,…”, ), and
the set of rules and/or policies is associated with a configuration of a set of edge devices operating in local data communication with a set of energy generation facilities, energy storage facilities, energy delivery facilities or energy consumption systems (see [0016] “These methods and systems include methods, systems, components, devices, workflows, services, processes, and the like that are deployed in various configurations and locations, such as: (a) at the “edge” of the Internet of Things, such as in the local environment of a heavy industrial machine;… as well as methods and systems for deploying increased intelligence at the edge, in the network, and in the cloud or premises of the controller of an industrial environment.”; also, see [0331] “…The policy automation engine 4032 may apply the policies according to one or more models, such as based on the characteristics of a given device, machine, or environment. For example, a large machine, such as a machine for power generation, may include a policy that only a verifiably local controller can change certain parameters of the power generation,…” local controller suggest an edge controller; also, see [0335] and [0345]; also, see [2069]).
As per claim 21, Cella-Li teaches the AI-based platform of claim 1, wherein the heterogeneous set of energy types includes at least two of (only two types below are required),
a transportable energy type for which the provisioning of energy resources includes provisioning at least one portable energy storage system to transport stored energy for the delivery of energy to the point of consumption,
a renewable energy type for which the provisioning of energy resources includes provisioning a quantity of energy generated by at least one renewable energy generator for the delivery of energy to the point of consumption (see Fig. 282-283 solar energy plant 5709, wind energy plant 5711; 0316 “In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from energy production environments, such as thermal, nuclear, geothermal, chemical, biomass, carbon-based fuels, hybrid-renewable energy plants, and the like. Many of these plants may use multiple forms of energy harvesting equipment like wind turbines, hydro turbines, and steam turbines powered by heat from nuclear, gas-fired, solar, and molten salt heat sources…”;),
a fuel energy type for which the provisioning of energy resources includes provisioning a quantity of fuel for the delivery of energy to the point of consumption (see Fig. 282 and 283 hydrogen is a fuel provided for consumption, see [2130-2134]),
a line transmissible type for which the provisioning of energy resources includes provisioning transmission lines for the delivery of energy to the point of consumption (see Fig. 282 transmission lines to deliver power), or
a wireless transmissible type for which the provisioning of energy resources includes provisioning a wireless transmission capability for the delivery of energy to the point of consumption.
As per claim 28, Cella-Li teaches the AI-based platform of claim 1, Cella further teaches wherein the load/(see Fig. 282-283 the power system delivers the heterogeneous set of energy types in an off-grid configuration and/or independently of an energy grid). Cella teaches a data center (01414).
Li also teaches wherein the data center is off-grid, and the energy resources provisioned for delivering each respective energy type of the heterogeneous set of energy types are determined to deliver each energy type to the data center independently of an energy grid (see Fig. 8 the system is disconnectable from the backup utility grid 806 (off-grid), with an off-grid energy generation system such as solar and storage system 812; see Col 8 lines 62-67; also, see Fig. 26 one of the data centers is independent of the utility grid and depends on storage energy/batteries 2609, wind and/or solar energy, and a local generator, see Col 23 lines 23-38).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella-Li’s combination as taught above to include a data center is off-grid, and the energy resources provisioned for delivering each respective energy type of the heterogeneous set of energy types are determined to deliver each energy type to the data center independently of an energy grid as taught by Li in order to provide power to off-grid load such as data centers in order to save costs and reduce carbon emissions (see Figs 8 and 26 by using renewable sources and batteries carbon dioxide is reduced).
As per claim 32, Cella teaches a system for enabling intelligent orchestration and management of power and energy (see Fig. 6 and see [0216] and [0219] platform 100; and see [0264], [0312]-[0316], [0326] “…machine learning…”; also, see 0344] “.In embodiments, a platform is provided having training AI models based on industry-specific feedback … In embodiments, the various embodiments of cognitive systems disclosed herein may take inputs and feedback from industry-specific and domain-specific sources 116 (such as relating to optimization of specific machines, devices, components, processes, and the like)… This may include optimization of efficiency (such as in electrical, electromechanical, magnetic, physical, thermodynamic, chemical and other processes and systems), optimization of outputs (such as for production of energy, materials, products, services and other outputs) …” and see [0402]; also, see [1401]; also, see also, see Fig. 282 and 284 and [2130-2131] “…embodiments of the methods and systems related to renewable energy sources for hydrogen production, storage, distribution and use are depicted… Hydrogen production, storage, distribution, and use may be at least partially powered by one or more renewable energy sources, such as solar energy source 5709, wind energy source 5711, hydro energy source 5713, geothermal energy source 5715, and the like…”; also, see [2122-2137], [2150-2157], [2162] “…When a renewable energy source is available, yet hydrogen production is not called for (e.g., sufficient supply is stored, or an amount that is anticipated to be needed, such as based on machine learning or the like of prior local hydrogen demand over time is expected to be producible before needed), then electricity or the like produced from the renewable energy source could be fed back into the smart grid…”, thus, the delivery of energy is improved), comprising:
a data centersee [1414], [01422]), wherein the data center processes at least one workload associated with a consumption of energy (dace centers process or execute workloads which consume energy); and
a set of autonomous orchestration systems for improving delivery of a heterogeneous set of energy types to a point of consumption associated with the data center, wherein the delivery is based on (see claim 1 above; also, see [0216], [0219], [0313]-[0315], [0326], also, see also, see Fig. 164 and see [01399-01401], [1411], and see Fig. 282 and 284 and [2130-2131], [2122-2137], [2150-2157], [2162]) [),
a location of the point of consumption (see [01399] “ The self-organization functionality, in embodiments involving a neural net or other machine learning system…location of power sources..”; also, see Fig. 164 and see [01400-01401]; also, see Fig. 282 and [2122-2137], [2150-2157] [2162], emphasis in [2151] “…Likewise, sources of energy for operating a hydrolyzer and the like as described herein, such as renewable energy from solar and wind may be managed so that available sunlight and/or the wind may be tied to hydrogen production demand predictions from users such as industrial and others. In embodiments, this may facilitate ensuring allocation of available hydrogen for grid stability and the like. In embodiments, sensors that measure integrated energy use may similarly provide information to further facilitate managing for grid stability, among other things. In examples, predicted demand may be used in determining when and how much hydrogen should be produced and whether it should be stored to facilitate grid stability. In embodiments, this information may be used when portions of a grid are predicted to have high demand, while other portions are predicted to have low demand…”, portions of a grid suggests a location on the grid),
a set of consumption attributes including at least one of (this requires only one of the following attributes since it is in the alternative),
a peak power requirement at the point of consumption (see [01117] “…peak energy consumption…”; also, see Fig. 164 and see [01400-01401]; also, see Fig. 282 and [2122-2137], [2150-2157] [2162], emphasis in [2151] “…Likewise, sources of energy for operating a hydrolyzer and the like as described herein, such as renewable energy from solar and wind may be managed so that available sunlight and/or the wind may be tied to hydrogen production demand predictions from users such as industrial and others. In embodiments, this may facilitate ensuring allocation of available hydrogen for grid stability and the like. In embodiments, sensors that measure integrated energy use may similarly provide information to further facilitate managing for grid stability, among other things. In examples, predicted demand may be used in determining when and how much hydrogen should be produced and whether it should be stored to facilitate grid stability. In embodiments, this information may be used when portions of a grid are predicted to have high demand, while other portions are predicted to have low demand…”, high demands suggests peak power at certain point of consumption),
a continuity of power requirement at the point of consumption (see [01399] …power availability…”; see [2151] “…Likewise, sources of energy for operating a hydrolyzer and the like as described herein, such as renewable energy from solar and wind may be managed so that available sunlight and/or the wind may be tied to hydrogen production demand predictions from users such as industrial and others; also, see [2162] “…may be deployed as components in a smart power grid that may operate cooperatively with other components of a smart grid to attempt to deliver reliable energy available throughout the grid…When a renewable energy source is available, yet hydrogen production is not called for (e.g., sufficient supply is stored, or an amount that is anticipated to be needed, such as based on machine learning or the like of prior local hydrogen demand over time is expected to be producible before needed), then electricity or the like produced from the renewable energy source could be fed back into the smart grid”; also, see [2182] “…Energy sources that may be included in such an automated selection process may include solar energy, wind energy, hydrogen energy, sulfur dioxide, electricity (such as from an electricity grid), natural gas, and the like. In embodiments, an algorithm that may facilitate automatic energy selection may receive information about each energy source, such as availability,… to determine which energy source provides the best fit for operating the hydrolyzer in a given time period. By way of this example, the algorithm may favor energy sources that are more reliable, more available, and lower costs than those that are less reliable, less available, and costlier. In embodiments, combinations of these three factors may result in certain sources being selected. If a demand for reliable energy at a particular time is weighted more highly than price, for example, a costlier energy source may be automatically selected due to it being more reliably available”), and
a type of energy that can be used at the point of consumption (see [01401] “…type of equipment…”; also, see 2181 “..Example factors may include the price of other energy sources, including energy sources that are available to the cooking and heating system as well as those that are not directly available. In this way, selecting an energy source may be driven by other considerations, such as which energy source is better for the environment, and the like. In embodiments, an automatic energy source selection may be based, at least in part on the anticipated availability of an energy source…”), and
a provisioning of energy resources for the delivery of the heterogeneous set of energy types (see Fig. 282-283 the energy delivered to the load 5703 from a set of different energy resources for the delivery of the heterogeneous/diverse set of energy types; also, see [0278] “…The selected source of renewable energy may be based on characteristics of the environment; for example, marine industrial environments may have available wind and hydro-power, agricultural environments may have solar power, etc. …”; also, see [2151] “…sources of energy for operating a hydrolyzer and the like as described herein, such as renewable energy from solar and wind may be managed so that available…”; also, see [2157], [2162],), wherein the energy resources provisioned for delivering each energy type of the heterogeneous set of energy types is based on each energy type (this has been interpreted in the broadest reasonable interpretation in light of the disclosure as “wherein the energy resources provisioned for delivering each energy type of the heterogeneous set of energy types are based on each energy type that can be used at the point of consumption, each energy type that available/availability, or power generated”; see Fig. 282-283 and see [01401] “…type of equipment…”; also, see [2181] “..Example factors may include the price of other energy sources, including energy sources that are available to the cooking and heating system as well as those that are not directly available. In this way, selecting an energy source may be driven by other considerations, such as which energy source is better for the environment, and the like. In embodiments, an automatic energy source selection may be based, at least in part on the anticipated availability of an energy source…”).
While Cella teaches the systems are associated with a data center (see [1411]), and the platform is for the optimization of energy control/delivery to a load (see Figs. 164 and [1411] power generation environments; also, see Figs. 282-283 and see [2122-2134], [2185]), Cella does not explicitly teach the point of consumption of energy associated with a data center.
Li teaches an energy delivery optimization system comprising improving delivery heterogeneous of energy types to a point of consumption associated with a data center (see Fig. 8; see Col 4 lines 17-35 “… system level embodiments of renewable energy control systems (also referred to herein also as iSwitch) that address the operational overhead of a renewable energy powered data center…”; see Col 5 lines 5-10 “…Specifically, certain embodiments of SolarCore provide a joint optimization of green energy utilization and workload performance for multi-core processors, which comprise the mainstream hardware design choice for today's IT industries and demand increasing amounts of power to unleash their full computation potential…”; see Col 9 lines 48-50 “Certain optimization methods can be employed to maximize workload performance under the variable maximal power budget.”; also, see Col 11 lines 54 to Col 12 line 65; also, see Col 19 lines 49-61 “… In the description that follows, a data center design scheme is disclosed that integrates on-site renewable energy sources into the data center infrastructure…”; also, see Col 20 lines 20-23 “Certain embodiments of iSwitch provide a power management scheme that maintains a desirable balance between renewable energy utilization and data center performance.” And lines 32-52 “…iSwitch comprise a control architecture that provides an application-independent hierarchical control that leverages load migration to best utilize the renewable energy generation. Characterization of renewable power variability and data center load fluctuation reveals that power tracking may be done in a less frequent,…”; also, see Fig. 26 and Col 23 lines 23-38), wherein the data center processes at least one workload associated with the consumption of energy (see Col 16 lines 3-15; also, see claim 1 “a power manager configured to switch at least a portion of the plurality of servers between a renewable energy supply (RES) and a utility energy supply based at least in part upon a budget level corresponding to an amount of power available from the RES and a load power consumption of the at least one cluster”, power consumed by the cluster/data center is based on workloads execution; also, see Col 14 lines 35-48 “…Each benchmark was run in their representative execution intervals and the EPI is obtained by calculating the average-energy consumed per-instruction…”; also, see Col 25 line 61 to Col 26 line 8 and lines 48-56).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella’s invention to include improving delivery heterogeneous of energy to a point of consumption associated with a data center, wherein the data center processes at least one workload associated with the consumption of energy, wherein the data center processes at least one workload associated with the consumption of energy as taught by Li in order to provide heterogeneous of energy types to a data center in an optimized manner according to the load demand of the data center which has a variable power consumption load (see Col 6 lines 20-32 “certain embodiments of SolarCore include a multi-core architecture power management scheme that addresses a two-fold challenge, namely to maximize a multi-core processor's total solar energy utilization by performing load matching under variable PV power output and to intelligently allocate the dynamically varied power budget across multiple cores so that the maximum workload performance can be achieved”; see Col 5 lines 5-10; see Col 9 lines 48-50 “Certain optimization methods can be employed to maximize workload performance under the variable maximal power budget.”; also, see Col 11 lines 54 to Col 12 line 65; also, see Col 19 lines 49-61 “… In the description that follows, a data center design scheme is disclosed that integrates on-site renewable energy sources into the data center infrastructure…”; also, see Col 20 lines 20-23 “Certain embodiments of iSwitch provide a power management scheme that maintains a desirable balance between renewable energy utilization and data center performance” and lines 32-52 “…iSwitch comprise a control architecture that provides an application-independent hierarchical control that leverages load migration to best utilize the renewable energy generation. Characterization of renewable power variability and data center load fluctuation reveals that power tracking may be done in a less frequent,…”; also, see Fig. 26 and Col 23 lines 23-38) and apply the AI platform of Cella to control the power sources in an optimized manner as well ( 0316 “In embodiments, the platform 100 may include the local data collection system 102 deployed in the environment 104 to monitor signals from energy production environments, such as thermal, nuclear, geothermal, chemical, biomass, carbon-based fuels, hybrid-renewable energy plants, and the like. Many of these plants may use multiple forms of energy harvesting equipment like wind turbines, hydro turbines, and steam turbines powered by heat from nuclear, gas-fired, solar, and molten salt heat sources…”; see Fig. 164 and see [01399-01401]) a neural network to improve or optimize power efficiency/resource utilization. This embodiment is a neural network/ai based platform that accepts several attribute/parameters to optimize the output or delivery of power of a system such as power system; also, see [1411] “he targets 12002 can be any form of machinery or component thereof in an industrial environment 12000…target 12002…include computer equipment…”; also, see also, see Fig. 282 and 284 and [2130-2131] “…embodiments of the methods and systems related to renewable energy sources for hydrogen production, storage, distribution and use are depicted… Hydrogen production, storage, distribution, and use may be at least partially powered by one or more renewable energy sources, such as solar energy source 5709, wind energy source 5711, hydro energy source 5713, geothermal energy source 5715, and the like…”; also, see [2122-2137], [2150-2157], [2162] “…When a renewable energy source is available, yet hydrogen production is not called for (e.g., sufficient supply is stored, or an amount that is anticipated to be needed, such as based on machine learning or the like of prior local hydrogen demand over time is expected to be producible before needed), then electricity or the like produced from the renewable energy source could be fed back into the smart grid…”, thus, the delivery of energy is improved) .
As to claim 33, this claim is the system claim corresponding to the system claim 21 and is rejected for the same reasons mutatis mutandis.
As to claim 40, this claim is the system claim corresponding to the system claim 28 and is rejected for the same reasons mutatis mutandis.
Claim(s) 7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al (US 20190033845 cited in the IDS) in view of Li et al (US 9218035) as applied to claim 5 above, and further in view of Steven (US 20160190805 cited in the IDS).
As per claim 7, Cella-Li teaches the AI-based platform of claim 5, Cella does not explicitly teach wherein the set of governance parameters relates to carbon generation or emissions.
However, Steven teaches a systems for improving delivery of energy (see Fig. 28 and [0092] “FIG. 28 shows an example energy storage asset optimization according to a principle described herein”) comprising optimizing/improving the coordination of different set of energy types based on a set of governance parameters relates to carbon generation or emissions (see [0027] “the mathematical model for the energy asset(s) is employed to determine a suggested operating schedule over a given time period T for the energy asset(s) (different than the BAU operating schedule) based on a mathematical optimization of an “objective cost function” representing the net energy-related cost to the energy customer for operating the asset(s)”; also, see [0028] “The objective cost function employed in the mathematical optimization to determine a suggested operating schedule for the energy asset(s) also may specify energy-related costs which are offset by the energy-related revenues. In particular, in some examples, the energy-related costs included in the objective cost function may include… emissions-related costs,…”; also, see [0102] “to provide energy asset management capabilities for reducing retail electricity costs by optimizing electricity usage, generation, and storage, while at the same time providing significant revenue opportunities in markets, including wholesale electricity markets, in regulation markets, in synchronized reserve markets and/or in emissions markets.”; also, see [0174] “…The emissions costs can be associated with greenhouse gas emissions during operation of the system. Non-limiting examples of such emissions are CO.sub.x emissions (e.g., carbon monoxide and carbon dioxide emissions)… the supply costs based on the emissions costs may be computed based on a trading price of an emissions credit based on an amount of emissions, such as but not limited to a trading price of a carbon credit based on CO.sub.x emission (also an economic benefit)…”; also, see [0175], [0355] “…wherein the operating schedule for the energy assets 126 is optimized for reducing energy costs, reducing emissions costs,…”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella-Li’s combination as taught above to include optimizing/improving the coordination of different set of energy types based on a set of governance parameters relates to carbon generation or emissions as taught by Steven in order to reduce costs and maximize the delivery of energy with respect to cost (see [0027-0028] and [0355]).
As per claim 20, Cella-Li teaches the AI-based platform of claim 1, but it does not explicitly teach wherein the set of autonomous orchestration systems is configured to determine the delivery of the heterogeneous set of energy types based on a simulation of energy consumption by at least one energy consumer, the simulation is based on a data set that includes alternative state or event parameters for at least one of the at least one energy consumer that reflect alternative consumption scenarios, and the simulation is based on a demand response model that accounts for how energy demand responds to changes in a price of energy or a price of an operation or activity for which the energy is consumed.
However, Steven teaches a system for improving delivery of energy (see Fig. 28 and [0092] “FIG. 28 shows an example energy storage asset optimization according to a principle described herein”; also, see [0027-0028];) comprising a set of autonomous orchestration systems is further configured to determine the delivery of the heterogeneous set of energy types based on a simulation of energy consumption by at least one energy consumer (see [0010] “These energy assets include energy storage assets, energy consuming assets and energy generating assets. In different examples herein, an energy asset can include an energy storage asset, an energy consuming asset, and/or an energy generating asset”; also, see [0019] “…the mathematical model, which in turn provides as an output a simulated CBL energy profile representing a typical electricity consumption or generation as a function of time, over a given time period T, for the modeled energy asset(s)….”; also, see [0040] “In an example, the dynamic simulation model of the energy profile of the at least one energy asset is a semi-linear regression over at least one of the model parameters. In this example, the dynamic simulation model of the energy profile of the at least one energy asset is a semi-linear regression over at least one of a zone temperature of the at least one energy asset, a load schedule of the at least one energy asset, the projected environmental condition, and a control setpoint of the at least one controllable energy asset” ), the simulation is based on a data set that includes alternative state or event parameters for at least one of the at least one energy consumer that reflect alternative consumption scenarios (see [0027] the simulation is based on different costs or revenue, “the mathematical model for the energy asset(s) and specifies energy-related revenues from one or more wholesale energy markets (e.g., based on forecasted wholesale energy prices over the time period T for the one or more wholesale markets of interest), from which possible revenue may be available to the energy customer. In some examples, the energy-related revenues specified in the objective cost function may take into consideration a simulated customer baseline (CBL) energy profile (discussed above) as a basis for determining such revenue…”; also, see [0030]; also, see [0153], [0164], [0233], [0355], [0381]), and the simulation is based on a demand response model that accounts for how energy demand responds to changes in a price of energy or a price of an operation or activity for which the energy is consumed (see [0019] and [0023] “Accordingly, for at least the foregoing reasons, a simulated and predictive CBL energy profile, based on a mathematical model of an energy customer's energy asset(s) according to the concepts disclosed herein (rather than an historical actual-use-based CBL as conventionally employed), provides a significant improvement for more accurately determining revenue earned from economic demand response wholesale electricity market…”; see [0027] “the mathematical model for the energy asset(s) is employed to determine a suggested operating schedule over a given time period T for the energy asset(s) (different than the BAU operating schedule) based on a mathematical optimization of an “objective cost function” representing the net energy-related cost to the energy customer for operating the asset(s)”; also, see [0028] “The objective cost function employed in the mathematical optimization to determine a suggested operating schedule for the energy asset(s) also may specify energy-related costs which are offset by the energy-related revenues. In particular, in some examples, the energy-related costs included in the objective cost function may include… emissions-related costs,…”; also, see [0030] and [0033], [0040], [0096]).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella-Li’s combination as taught above to include the set of autonomous orchestration systems is further configured to determine the delivery of the heterogeneous set of energy types based on a simulation of energy consumption by at least one energy consumer, the simulation is based on a data set that includes alternative state or event parameters for at least one of the at least one energy consumer that reflect alternative consumption scenarios, and the simulation is based on a demand response model that accounts for how energy demand responds to changes in a price of energy or a price of an operation or activity for which the energy is consumed as taught by Steven in order to improve the energy delivery and consumption of energy assets including consuming energy assets and variety of power generation assets (see [0010] and [0096]-[0097], [0355]).
Claim(s) 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al (US 20190033845 cited in the IDS) in view of Li et al (US 9218035) as applied to claim 1 above, and further in view of Schmitt et al (US 20210110262, cited in the IDS).
As per claim 9, Cella-Li teaches the AI-based platform of claim 1, while the term digital twin has not been explicitly defined and while Cella teaches adaptive AI models (see 0326, 0343,) Cella does not explicitly teach further comprising an adaptive energy digital twin that represents at least one of, an energy stakeholder entity,
an energy distribution resource, 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 (the term “digital twin” will be interpreted in the BRI as a simulation, virtual model, or replicated model of a process of a machine, system , facility, etc.).
However, Schmitt teaches a system comprising an adaptive energy digital twin that represents (see [0010] and [0051]) at least one of, an energy stakeholder entity, an energy distribution resource, 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 (see [0010] “…digital twin simulations can be efficiently used by running a digital twin simulation in parallel to the normal operation of the technical system. Both, the digital twin simulation and the technical system operating in the physical world, share the same environmental input quantities. It is possible to compare sensor measurements from the real, physical technical system to a corresponding output from the digital twin simulator…”; also, see [0051] “The digital twin simulation integrates artificial intelligence, machine learning and software analytics with spatial network graphs to generate a digital simulation model that updates and changes as its physical counterpart changes. The digital twin simulation continuously learns and updates itself from multiple sources to represent its near real-time status, working condition or position…”; and [0121]-[0123] “For implementing the system for detecting an anomalous operating status, the energy management system and its installations and the facility were modelled with a digital twin simulation, which is realized is using the Modelica-based SimulationX tool together with the Green City library for modelling of building energy systems and e-mobility applications….”; also, see [0106] “comprise components:…[0107-0108] cooling system…describe energy consumption…. [0109] CHP or cogeneration, [0111] a photovoltaic system, [0112] vehicles charging stations…electrical power consumption; all of these devices at least represent an energy stakeholder entity or an energy distribution resource).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella-Li’s combination as taught above to include an adaptive energy digital twin that represents at least one of, an energy stakeholder entity, an energy distribution resource, 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 as taught by Schmitt in order to execute a digital twin of an entity or component of a system to compare real values with simulated values from the digital twin to conclude that an anomalous situation or event has occurred (see [0010], and [0012]).
As per claim 10, Cella-Li teaches the AI-based platform of claim 1, while the term digital twin has not been explicitly defined and while Cella teaches adaptive AI models (see 0326, 0343,) Cella does not explicitly teach comprising an adaptive energy digital twin that is configured to perform at least one of, providing a visual indicator of energy consumption by at least one energy consumer, analytic indicator of energy consumption by at least one energy consumer, filtering energy data, highlighting energy data, or adjusting energy data.
However, Schmitt teaches a system comprising an adaptive energy digital twin (see [0010] and [0051]) that is configured to perform at least one of, providing a visual indicator of energy consumption by at least one energy consumer, analytic indicator of energy consumption by at least one energy consumer, filtering energy data, highlighting energy data, or adjusting energy data (see [0010] “…digital twin simulations can be efficiently used by running a digital twin simulation in parallel to the normal operation of the technical system. Both, the digital twin simulation and the technical system operating in the physical world, share the same environmental input quantities. It is possible to compare sensor measurements from the real, physical technical system to a corresponding output from the digital twin simulator…”; also, see [0051] “The digital twin simulation integrates artificial intelligence, machine learning and software analytics with spatial network graphs to generate a digital simulation model that updates and changes as its physical counterpart changes. The digital twin simulation continuously learns and updates itself from multiple sources to represent its near real-time status, working condition or position…”; and [0121]-[0123] “For implementing the system for detecting an anomalous operating status, the energy management system and its installations and the facility were modelled with a digital twin simulation, which is realized is using the Modelica-based SimulationX tool together with the Green City library for modelling of building energy systems and e-mobility applications….”; also, see [0106] “comprise components:…[0107-0108] cooling system…describe energy consumption…. [0109] CHP or cogeneration, [0111] a photovoltaic system, [0112] vehicles charging stations…electrical power consumption; all of these devices at least represent an energy consumer, thus, the digital twin provides analytic indicator of energy consumption).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella-Li’s invention to include an adaptive energy digital twin that is configured to perform at least one of, providing a visual and/or analytic indicator of energy consumption by at least one energy consumer, filtering energy data, highlighting energy data, or adjusting energy data as taught by Schmitt in order to execute a digital twin of an entity or component of a system to compare real values with simulated values from the digital twin to conclude that an anomalous situation or event has occurred (see [0010], and [0012]).
As per claim 11, Cella-Li teaches the AI-based platform of claim 1, while the term digital twin has not been explicitly defined and while Cella teaches adaptive AI models (see 0326, 0343,) Cella does not explicitly teach comprising an adaptive energy digital twin that is configured to generate at least one of a visual or an analytic indicator of energy consumption by at least one of, at least one machine, at least one factory, or at least one vehicle in a vehicle fleet.
However, Schmitt teaches a system comprising an adaptive energy digital twin that is configured to generate at least one of a visual or analytic indicator of energy consumption by at least one of, at least one machine, at least one factory, or at least one vehicle in a vehicle fleet (see [0010], [0051] The digital twin simulation integrates artificial intelligence, machine learning and software analytics with spatial network graphs to generate a digital simulation model that updates and changes as its physical counterpart changes. The digital twin simulation continuously learns and updates itself from multiple sources to represent its near real-time status, working condition or position…”; and [0121]-[0123] “For implementing the system for detecting an anomalous operating status, the energy management system and its installations and the facility were modelled with a digital twin simulation, which is realized is using the Modelica-based SimulationX tool together with the Green City library for modelling of building energy systems and e-mobility applications….”; also, see [0106] “comprise components:…[0107-0108] cooling system…describe energy consumption…. [0109] “ a combined heat and power (CHP or cogeneration) device with sensors providing time-series values for electrical power consumption and thermal power output of the cogeneration device respectively”; [0111] a photovoltaic system, [0112] vehicles charging stations…electrical power consumption; all of these devices at least represent an energy consumer, thus, the digital twin provides analytic indicator of energy consumption).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella-Li’s invention to include an adaptive energy digital twin that is configured to generate a visual and/or analytic indicator of energy consumption by at least one of, at least one machine, at least one factory, or at least one vehicle in a vehicle flee as taught by Schmitt in order to execute a digital twin of an entity or component of a system to compare real values with simulated values from the digital twin to conclude that an anomalous situation or event has occurred (see [0010], and [0012]).
Claim(s) 13-14 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al (US 20190033845 cited in the IDS) in view of Li et al (US 9218035) as applied to claim 1 above, and further in view of Forbes Jr et al (US 20210090185, cited in the IDS).
As per claim 13, Cella-Li teaches the AI-based platform of claim 1, while Cella further teaches wherein at least one consumption attribute of the set of consumption attributes is based on at least one (see [1077] and see [2182] “…algorithm that may facilitate automatic energy selection may receive information about each energy source, such as availability, costs, efficiency, and the like that may be processed by, for example comparing the information to determine which energy source provides the best fit for operating the hydrolyzer in a given time period…”; also, see [2183], [2185], [2188] “In embodiments, integration and/or access to data processing systems that also have access to third-party data may be included in the methods and systems described herein. By monitoring data collected from sensors, time of day, weather conditions, and other data sources may be used with specific rule sets to trigger activation and/or stoppage of hydrogen use…”). While the term public data resource has been exemplified as any data collected from the grid affecting consumption or even a sensor, While Cella teaches at least one of the consumption attributes is based on at least one data resource, Cella does not explicitly teach the data resource is public, wherein the public data resources including 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, or an ecommerce data resource.
However, Forbes teaches a system and method comprising platform comprising a set of orchestration systems improving the delivery of energy based on a set of consumption attributes (see [0013], [0135], [0141], [0159], [0161], [0207] and [0214]), at least one consumption attribute of the set of consumption attributes is based on at least one public data resource, the at least one public data resource includes at least one of: a weather data resource (0227), a satellite data resource, a census data resource, a population data resource, a demographic data resource, a psychographic data resource, a market data resource, or an ecommerce data resource (see [0227] “In level 1 (L1) of the present invention, the user may provide additional information to the system and/or additional information may be gathered from public sources to further populate the user profile. Information regarding the plurality of variability factors may obtained from public sources. For example, information regarding weather (e.g., outside temperature, sunlight, humidity, wind speed and direction) may be obtained from publicly available weather services…”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella-Li’s combination as taught above to include a set of orchestration systems improving the delivery of energy based on a set of consumption attributes (see [0013], [0135], [0141], [0159], [0161], [0207] and [0214]), at least one consumption attribute of the set of consumption attributes is based on at least one public data resource, the at least one public data resource includes at least one of: a weather data resource (0227), a satellite data resource, a census data resource, a population data resource, a demographic data resource, a psychographic data resource, a market data resource, or an ecommerce data resource as taught by Forbes in order to perform a more accurate prediction of energy consumption by including public data resources (see [0222] and [0227] and [0229]).
As per claim 14, Cella-Li teaches the AI-based platform of claim 1, While Cella teaches that the platform can be located in an enterprise system and planning data resources (see 2061 or cloud systems), Cella does not explicitly teach wherein at least one of consumption attributes of the set of consumption attributes is based on at least one enterprise data resource, and
the at least one enterprise data resource includes at least one of,
resource planning data, sales data, marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.
However, Forbes teaches a system and method comprising a platform comprising a set of orchestration systems improving the delivery of energy based on a at least one consumption attribute of a set of consumption attributes (see [0013], [0135], [0141], [0159], [0161], [0207] and [0214]), wherein the at least one consumption attribute is based on at least one enterprise data resource, and the at least one enterprise data resources includes at least one of, resource planning data, sales data, marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data (see [164] “…the EnergyNet Market Interface connects with regulation agencies, for example ERCOT and other RTOs, for signaling and pricing. The Energy Supplier can be IOU, REP, and/or Municipal power agencies. The Utility Infrastructure at the Energy Supplier provides applications, such as VPP, Distribution Management System (DMS), and DER applications, and Utility Enterprise Infrastructure. The Utility Enterprise Infrastructure communicates with the SOA and SDK services at the Causam data center via IPSec and/or VPN for standard or customer SOA integration… Third Party Infrastructure includes SOA for utility enterprise, consumer information, general ledger, accounting, billing, payment, banks, marketing, strategy, capitalization and investment” Also, see Fig. 16 enterprise resources including marketing data, pricing data, customer data/agreements, also, see [0165] “…there are four key elements within the EnergyNet enterprise financial settlement product: data ingress, market participation, payments collection and advanced energy settlements…”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella-Li’s combination as taught above to include comprising a set of orchestration systems improving the delivery of energy based on a set consumption attributes, wherein at least one consumption attribute of the set of consumption attributes is based on at least one enterprise data resource, the at least one enterprise data resources includes at least one of, resource planning data, sales data, marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data as taught by Forbes in order to perform a more accurate prediction of energy consumption by including enterprise data resources (see [0161], [0164-0165], [0222] and [0227] and [0229]).
As per claim 17, Cella-Li teaches the AI-based platform of claim 1,
While Cella teaches wherein at least one of the set of autonomous orchestration systems is configured to record, in a distributed ledger (see [0372], [1415] “ledger”; [1533]), Cella does not explicitly teach the data comprises at least one energy-related event, and the at least one energy-related event includes at least one of, an energy purchase event, an energy sale event, a service charge associated with an energy purchase, a service charge associated with an energy sale event, an energy consumption event, 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.
However, Forbes teaches a system and method comprising a platform comprising a set of orchestration systems improving the delivery of energy based on a consumption attribute (see [0013], [0135], [0141], [0159], [0161], [0207] and [0214]), and wherein at least one of the set of autonomous orchestration systems is configured to record, in a distributed ledger at least one energy-related event, the at least one energy-related event includes at least one of, an energy purchase event,
an energy sale event, a service charge associated with an energy purchase event,
a service charge associated with an energy sale event (see [0255-0256] and [0259]), an energy consumption event (see 0014 the data includes demand side), an energy generation event (see 0014 the data includes supply), an energy distribution event, an energy storage event, a carbon emission production event, a carbon emission abatement event, a renewable energy credit event (see [0277]), a pollution production event, or a pollution abatement event (see Abstract “…The energy related data of the multiplicity of active grid elements are based on measurement and verification. The energy related data and the settlement related data are validated and recorded on a distributed ledger with a time stamp and a geodetic reference”; see [0012] “The present invention provides for systems, methods, and graphic user interface embodiments for providing electric power usage (past, current, and/or future projected) information, management, financial settlements, and messaging, and applications as described herein. In addition, the present invention provides for the use of blockchain technologies that provide for market based electric power usage (past, current, and/or future projected) information collection, management, tokens, financial settlements, alternative currencies such as “crypto currencies”, distributed databases, distributed general ledgers and secure messaging distributed amongst coordinators and data processing nodes as described herein”; also, see [0014], [0247], [0255]).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella’s invention to include at least one of the set of autonomous orchestration systems configured to record, in a distributed ledger at least one energy-related event, the at least one energy-related event including at least one of, an energy purchase event, an energy sale event, a service charge associated with an energy purchase event, a service charge associated with an energy sale event, an energy consumption event, 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 as taught by Forbes in order to record, maintain and transmit data in a secure manner (see [0012] “distributed general ledgers and secure messaging distributed amongst coordinators and data processing nodes as described herein”; also, see [0141] “FIG. 3 illustrates EnergyNet features in the present invention. EnergyNet is a secure and dynamic marketplace ecosystem enabling energy consumers, distributed generators, utility service providers, equipment providers and application developers to participate in financial and electrical service transactions...”).
Claim(s) 22-25 and 34-37 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al (US 20190033845 cited in the IDS) in view of Li et al (US 9218035) as applied to claim 1 above, and further in view of Mahindru et al (US 11169592).
As per claim 22, Cella-Li teaches the AI-based platform of claim 1, Cella further teaches wherein, the delivery of at least a portion of the heterogeneous set of energy types to the point of consumption is based on a timing of energy demand at the point of consumption (see [0315], [0340] “. In embodiments, the data pricing system 4112 may use a set of rules, models, or the like, such as setting pricing based on supply conditions, demand conditions, pricing of various available sources, and the like. For example, pricing for a package may be configured to be set based on the sum of the prices of constituent elements (such as input sources, sensor data, or the like), or to be set based on a rule-based discount to the sum of prices for constituent elements, or the like. Rules and conditional logic may be applied, such as rules that factor in cost factors (such as bandwidth and network usage, peak demand factors…”; [1401] “. The self-organization functionality may be seeded with a model for operation of the system of generators in a manner that results in a specified profit goal, such as indicating an on/off state for individual generator(s) in the power generation system based on the time of day, current market sale price for the fuel consumed by the generators, current demand or anticipated future demand, and the like…”; also, see [2069], [2676], [2080], [2151], [2162] “…hydrogen demand over time…”), and Cella does not explicitly teach the timing is determined by an energy provisioning and governance solution associated with the data center based on a priority of a workload of the data center (this has been interpreted as the delivery of at least a portion of the heterogeneous set of energy types to the point of consumption is based on a timing of energy demand at the point of consumption, the timing is determined based on a priority of a workload of the data center).
Li further teaches the energy delivery optimization system further comprising delivery of at least a portion of the heterogeneous set of energy types to the point of consumption is based on a timing of energy demand at the point of consumption, the timing is determined by an energy provisioning and governance solution associated with the data center based on a workload of the data center (see Col 3 line 60 to Col 4 line 16 “With respect to SolarCore, certain embodiments are disclosed that include a solar energy driven, multi-core architecture power management scheme that combines maximal power provisioning control and workload run-time optimization…”; see Col 5 lines 5-11 “Specifically, certain embodiments of SolarCore provide a joint optimization of green energy utilization and workload performance for multi-core processors, which comprise the mainstream hardware design choice for today's IT industries and demand increasing amounts of power to unleash their full computation potential”; see Col 14 line 35 to Col 15 line 10; see Col 27 lines 1-30; see Fig. 11 and see Col 12 lines 38-56; also, see Col 25 lines 30-42 “…During each re-shuffling interval, the average load utilization (lazy tracking) is recorded in a fine-grained time interval (e.g., 1 min) and is used to predict the load for the next period. Upon rescheduling, the optimizer 2906 in the iSwitch scheduler updates the baseline switch operations of each server cluster in SAB 2903 with the goal of mitigating the likelihood of severe load power fluctuation in the next control period…”, thus, a time of energy delivery is predicted for a future period based on a workload of a data center; see Col 25 line 61 to Col 26 line 5; also, see Col 31 lines 1-47).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella-Li’s combination as taught above to include delivery of at least a portion of the heterogeneous set of energy types to the point of consumption is based on a timing of energy demand at the point of consumption, the timing is determined by an energy provisioning and governance solution associated with the data center based on a workload of the data center as taught by Li in order to in order to provide heterogeneous of energy types to a data center in an optimized manner according to the load demand of a workload of the data center which has a variable power consumption load (see Col 6 lines 20-32; see Col 5 lines 5-10; see Col 9 lines 48-50 also, see Col 11 lines 54 to Col 12 line 65; also, see Col 19 lines 49-61 “… In the description that follows, a data center design scheme is disclosed that integrates on-site renewable energy sources into the data center infrastructure…”; also, see Col 20 lines 20-23 “Certain embodiments of iSwitch provide a power management scheme that maintains a desirable balance between renewable energy utilization and data center performance” and lines 32-52 “…iSwitch comprise a control architecture that provides an application-independent hierarchical control that leverages load migration to best utilize the renewable energy generation. Characterization of renewable power variability and data center load fluctuation reveals that power tracking may be done in a less frequent,…”; also, see Fig. 26 and Col 23 lines 23-38).
While Li teaches that some cores of the data center are given priority of power based on a priority (see Col 12 lines 20-21 “The cores that exhibit large TPR have higher priority to receive the available power”), Cella-Li does not explicitly teach the timing (interpreted as timing of energy demand), is determined based on a priority of a workload of the data center (e.g. the time of delivery of energy is determined for a workload with a priority).
Mahindru teaches a power management system comprising delivery of a portion of energy to a point of consumption is based on a timing/time of energy demand at the point of consumption (see Col 6 lines 33-37 and Col 7 lines 1-15; also, see Col 8 lines 25-48 workloads are predicted for certain times, also budgets of energy are allocated for certain periods at a point of consumption ), the timing is determined based on a priority of a workload of the data center (see Col 6 lines 33-37 and Col 7 lines 1-15; also, see Col 8 lines 25-48 “In various described embodiments herein, the present invention uses existing prediction techniques to estimate a workload's demand and allocates available power, or removes the workload's allocated power (based on a workload priority), which would be wasted, otherwise. A total available power is monitored and tracked. Power is maintained at as small variations as possible from the contracted utility power for the datacenter. In some embodiments, the total power available is allocated to workloads in need based on the workload's priority to provide them an additional throughput boost, while keeping the overall utilization within the range power contracted for such datacenter… the system removes the estimated surplus power and/or reduces the voltage allocated to some of the workloads (with lower priority) and allocates it to the higher prioritized workloads based on their predicted demand…”; see Col 21-22 claim 1 “…backup electrical power is dynamically allocated to the respective processors performing the respective workloads based upon a priority of the SLA of the respective workloads… allocating the backup electrical power, in real-time with no interruption to the respective workloads, to respective individual cores of the respective processors such that, according to the SLA of the respective workloads, a first one of the respective individual cores is allocated a first amount of backup electrical power and a second one of the respective individual cores is allocated a second amount of backup electrical power notwithstanding whether the first amount and the second amount are identical”; also, see Col 4 lines 28-35 “…data center..”).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella-Li’s combination as taught above to include delivery of a portion of energy to a point of consumption is based on a timing/time of energy demand at the point of consumption, the timing is determined based on a priority the timing is determined based on a priority of a workload of the data center as taught by Mahindru in order to control the portion of power delivery and reduce cost by efficiently managing power and workloads based on their priorities (see Col 4 lines 56-61; also, see Col 13 line 62 to Col 14 line 13 “…During the backup power generation, the aim is to save as much as possible the fuel needed to power the backup generator(s) and if a service or resource pool does not use or need the backup power, to shut it off to be later restarted within milliseconds based on the disaggregated architecture. For services/workloads/resource pools which are contracted to run on backup power, the speed to execute the service would be the minimal possible to sustain the real-time throughput needed by that service and its processing. Hence constant adjustments for clock speed and voltage are needed to lower the energy consumption of that service/workload/resource pool and only increase such when the throughput detected is slower than that needed for the real-time processing delivery as contracted…”) in events when energy capacity is reduced (see Col 13 line 29-40 “For example, during a “browning” of the utility power grid, an outright power failure requiring the need of Uninterruptible Power Supply (UPS) or generator usage, or other situations where the input power to the datacenter is not at full capacity, power may be re-allocated from the processors (adjusting the voltage and clock speed therein) performing lower priority workloads to higher priority workloads, dependent upon SLA requirements of the respective workloads being performed.”)
As per claim 23, Cella-Li-Mahindru teaches the AI-based platform of claim 22, Cella further teaches an adaptive energy data pipeline (see Fig. 22 multiplexer 5032 and streaming capability 5028, converter 5030 for a pipeline where the data collected is adapted including filtering, data routing, data processing, see [0414-0416]; also, see [0017] “data from multiple sensors are multiplexed at the device for storage of a fused data stream… including self-organizing network coding for a data network that transports data from multiple sensors in an industrial data collection environment.”; see [0712] “The selection of the plurality of sensors 9006 for a data monitoring device 9000 designed for a specific component or piece of equipment may depend on a variety of considerations such as accessibility for installing new sensors, incorporation of sensors in the initial design, anticipated operational and failure conditions, resolution desired at various positions in a process or plant, reliability of the sensors, power availability, power utilization, …”, this teaches an adaptive pipeline component which adapts the inputs; also, see [1241] “ Power generation energy storage may be monitored with sensors that capture data related to storage and use of stored energy. Information such as utilization of individual energy storage cells, energy storage rate (e.g., battery charging and the like), stored energy consumption rate (e.g., KWH being supplied by an energy storage system), storage cell status, and the like may be captured and converted into augmented reality viewable attributes that may be presented in an augmented reality view of an energy storage system”; also, see [1491]), Cella does not explicitly teach wherein, the timing (of energy demand) is further based on a delivery of data to the data center by an adaptive energy data pipeline, and the workload includes processing the data delivered by the adaptive energy data pipeline to the data center (this has been interpreted as “the timing of energy demand is further based on data to be delivered data to the data center by an adaptive energy data pipeline, and the workload includes processing the data to be delivered by the adaptive energy data pipeline to the data center”).
Li further teaches the system comprises the timing (of energy demand) is further based on a delivery of data to the data center (this has been interpreted as “the timing of energy demand is further based on data to be delivered data to the data; Li teaches data or workload to executed at a data center, see Col 17 lines 24-35 and lines 40-45 “. The energy utilization (i.e., actual total solar energy consumed/theoretical maximum solar energy supply) was calculated with various load adaptation scenarios. FIG. 18 illustrates examples of average energy utilization across different geographical locations”, load adaptation is data increased or decreased to be executed, see Col 8 lines 12-14 “… the load adaptation scheme (i.e. increasing or decreasing the load) varies with different operating point positions, as shown in FIGS. 5A and 5B…” ), the workload includes processing the data delivered to the data center (see Col 14 lines 65-67 “The fixed-power is a non-tracking power management scheme which assumes a constant power budget during the entire workload execution”; see Col 16 lines 3-15).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella-Li-Mahindru’s combination as taught above to include the timing (of energy demand) is further based on a delivery of data to the data center, the workload includes processing the data delivered to the data center as taught by Li in order to provide heterogeneous of energy types to a data center in an optimized manner according to the load demand of the data center which has a variable power consumption load (see Col 6 lines 20-32; see Col 5 lines 5-10; see Col 9 lines 48-50; also, see Col 11 lines 54 to Col 12 line 65; also, see Col 19 lines 49-61 “… In the description that follows, a data center design scheme is disclosed that integrates on-site renewable energy sources into the data center infrastructure…”; also, see Col 20 lines 20-23 “Certain embodiments of iSwitch provide a power management scheme that maintains a desirable balance between renewable energy utilization and data center performance” and lines 32-52 “…iSwitch comprise a control architecture that provides an application-independent hierarchical control that leverages load migration to best utilize the renewable energy generation. Characterization of renewable power variability and data center load fluctuation reveals that power tracking may be done in a less frequent,…”; also, see Fig. 26 and Col 23 lines 23-38) and apply the Adaptive energy data pipeline of Cella to transmit the data or workload to the data center.
As per claim 24, Cella-Li-Mahindru teaches the AI-based platform of claim 23, Li further teaches wherein the delivery of the data to the data center by the adaptive energy data pipeline is based on a timing of the delivery of at least a portion of the heterogeneous set of energy types to the point of consumption associated with the data center (see Li teaches data or workload to executed at a data center, see Col 17 lines 24-35 and lines 40-45 “. The energy utilization (i.e., actual total solar energy consumed/theoretical maximum solar energy supply) was calculated with various load adaptation scenarios. FIG. 18 illustrates examples of average energy utilization across different geographical locations”, load adaptation is data increased or decreased to be executed, see Col 8 lines 12-14 “… the load adaptation scheme (i.e. increasing or decreasing the load) varies with different operating point positions, as shown in FIGS. 5A and 5B…” ), the workload includes processing the data delivered to the data center (see Col 14 lines 65-67 “The fixed-power is a non-tracking power management scheme which assumes a constant power budget during the entire workload execution”; see Col 16 lines 3-15 ).
As per claim 25, Cella-Li-Mahindru teaches the AI-based platform of claim 24, Cella teaches filtering of data (see [0271], 0335, 0395 “… filtering and other such functions can be implemented to extract data from the streaming devices 4740 that corresponds to the sourced data of the legacy instruments…”, 0414, 0437, 0454, 0662, 0813), But Cella does not explicitly wherein, the delivery of the data to the data center by the adaptive energy data pipeline is adapted to determine at least one property of the delivery of the data to the data center based on the timing of the delivery of at least a portion of the heterogeneous set of energy types to the point of consumption associated with the data center, and the at least one property includes at least one of, a volume of the data delivered to the data center by the adaptive energy data pipeline, a filtering of the data delivered to the data center by the adaptive energy data pipeline, a timing of the data delivered to the data center by the adaptive energy data pipeline, or a priority of the data delivered to the data center by the adaptive energy data pipeline.
Mahindru teaches the system further comprising the delivery of the data to the data center is adapted to determine at least one property of the delivery of the data to the data center based on the timing of the delivery of at least a portion of the heterogeneous set of energy types to the point of consumption associated with the data center (see Col 6 lines 33-37 and Col 7 lines 1-15; also, see Col 8 lines 25-48 workloads are predicted for certain times, also budgets of energy are allocated for certain periods at a point of consumption, see Col 6 lines 33-37 and Col 7 lines 1-15; also, see Col 8 lines 25-48 “In various described embodiments herein, the present invention uses existing prediction techniques to estimate a workload's demand and allocates available power, or removes the workload's allocated power (based on a workload priority), which would be wasted, otherwise. A total available power is monitored and tracked. Power is maintained at as small variations as possible from the contracted utility power for the datacenter. In some embodiments, the total power available is allocated to workloads in need based on the workload's priority to provide them an additional throughput boost, while keeping the overall utilization within the range power contracted for such datacenter… ), and the at least one property includes at least one of, a volume of the data delivered to the data center by the adaptive energy data pipeline, a filtering of the data delivered to the data center by the adaptive energy data pipeline, a timing of the data delivered to the data center by the adaptive energy data pipeline, or a priority of the data delivered to the data center by the adaptive energy data pipeline (see Col 6 lines 33-37 and Col 7 lines 1-15; also, see Col 8 lines 25-48 “In various described embodiments herein, the present invention uses existing prediction techniques to estimate a workload's demand and allocates available power, or removes the workload's allocated power (based on a workload priority), which would be wasted, otherwise. A total available power is monitored and tracked. Power is maintained at as small variations as possible from the contracted utility power for the datacenter. In some embodiments, the total power available is allocated to workloads in need based on the workload's priority to provide them an additional throughput boost, while keeping the overall utilization within the range power contracted for such datacenter…).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella-Li’s combination as taught above to include the delivery of the data to the data center is adapted to determine at least one property of the delivery of the data to the data center based on the timing of the delivery of at least a portion of the heterogeneous set of energy types to the point of consumption associated with the data center, and the at least one property includes at least one of, a volume of the data delivered to the data center by the adaptive energy data pipeline, a filtering of the data delivered to the data center by the adaptive energy data pipeline, a timing of the data delivered to the data center by the adaptive energy data pipeline, or a priority of the data delivered to the data center by the adaptive energy data pipeline as taught by Mahindru in order to control the portion of power delivery and reduce cost by efficiently managing power and workloads based on their priorities (see Col 4 lines 56-61; also, see Col 13 line 62 to Col 14 line 13 “) in events when energy capacity is reduced (see Col 13 line 29-40”).
As to claim 34, this claim is the system claim corresponding to the system claim 22 and is rejected for the same reasons mutatis mutandis.
As to claim 35, this claim is the system claim corresponding to the system claim 23 and is rejected for the same reasons mutatis mutandis.
As to claim 36, this claim is the system claim corresponding to the system claim 24 and is rejected for the same reasons mutatis mutandis.
As to claim 37, this claim is the system claim corresponding to the system claim 25 and is rejected for the same reasons mutatis mutandis.
Claim(s) 29-31 and 41-43 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al (US 20190033845 cited in the IDS) in view of Li et al (US 9218035) as applied to claim 1 above, and further in view of Hart et al (US 20090050591).
As per claim 29, Cella-Li’ teaches the AI-based platform of claim 1,Cella further teaches the delivery is based on a delivery of energy to a location of the point of consumption at a current and/or future time point (see [0326]; see also, see Fig. 164 and see [01399-01401]) a neural network to improve or optimize power efficiency/resource utilization. This embodiment is a neural network/ai based platform that accepts several attribute/parameters to optimize the output or delivery of power of a system such as power system; also, see [2122-2137], [2150-2157], [2162] “…When a renewable energy source is available, yet hydrogen production is not called for (e.g., sufficient supply is stored, or an amount that is anticipated to be needed, such as based on machine learning or the like of prior local hydrogen demand over time is expected to be producible before needed), then electricity or the like produced from the renewable energy source could be fed back into the smart grid…”, emphasis in [2151]; also, see [2181] “. Such predictions may also be based on prior experience regarding the availability of the source(s) of energy, which may be applied to machine learning algorithms that may provide predictions of future energy availability. Yet other factors that may be applied to an algorithm for automatically determining a source of energy may include availability of a source of water for producing hydrogen, availability of renewable energy (e.g., based on a forecast for sunlight, winds, and the like), level and/or intensity of need of the energy, anticipate level of need over a future period of time, such as the next 24 hours and the like. If an anticipate need over a future period of time includes large swings in demand over that timeframe, each peak in demand may be individually analyzed. Alternatively, an average or other derivatives of the demand over time may be used to determine a weighting for the various sources of energy.”). Li also teaches the delivery is based on a delivery of energy to a location of the point of consumption at a current and/or future time point (see Fig. 31 and see Col 25 lines 30-44 “FIG. 31 illustrates an example of a switch management optimization timeline of iSwitch. The controller re-shuffles the renewable energy powered servers (demand smoothing) at a coarse-grained time interval R (e.g., 15 minutes as the default value in some experiments). During each re-shuffling interval, the average load utilization (lazy tracking) is recorded in a fine-grained time interval (e.g., 1 min) and is used to predict the load for the next period. Upon rescheduling, the optimizer 2906 in the iSwitch scheduler updates the baseline switch operations of each server cluster in SAB 2903 with the goal of mitigating the likelihood of severe load power fluctuation in the next control period. Each switch tuning invoked by CSC 2703 will be assigned based on the updated SAB 2903).
While Cella teaches providing energy for industrial system including remote operations (see 0009 and [1077]), Cella-Li does not explicitly teach wherein, the point of consumption is mobile.
Hart teaches a system and method comprising providing/delivering power to a point of consumption associated with a data center, wherein the point of consumption associated is mobile (see Fig. 1 and see [0018] “ According to another aspect of the invention the mobile data center unit comprises…”; see [0033] “…. Additionally, or alternatively, the power management system 120 can utilize a generator, batteries, solar panels, or the like, to provide electricity when external supplies are unavailable, or provide insufficient power. This allows the data processing systems to be used in a variety of remote environments; also, see [0055] “the power management system 120 includes a generator 420, a set of batteries 421 and optionally a roof mounted solar panels shown generally at 422. The power management system is connected to the processing units via appropriate cables 423, 424 as will be described in more detail below. Connection (not shown) to an external power supply may also be provided) and controlling the delivery of energy to the point of consumption, the delivery is based on a delivery of energy to a location of the point of consumption at a current and/or future time point (see Fig. 3 delivery of energy at a current time based on current conditions; also, see [0018]).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella-Li’s combination as taught above to include providing/delivering power to a point of consumption associated with a data center, wherein the point of consumption associated is mobile, and controlling the delivery of energy to the point of consumption, the delivery is based on a delivery of energy to a location of the point of consumption at a current and/or future time point as taught by Hart in order to provide portable computing resources in location where service infrastructure, space or deployment-time is limited or otherwise compromised (see [0004]-[0005]) and optimize the power delivery based on current conditions or the point of consumption (see Fig. 3 and see [0114], [0156]).
As per claim 30, Cella-Li-Hart teaches the AI-based platform of claim 29, Cella further teaches wherein the point of consumption that is mobile includes at least one of,
a mobile data collector that collects data for the data center (see [0622] “Switching may involve activating a system to obtain additional data, such as moving a mobile system (such as a robotic or drone system), to a location where different or additional data is available, such as positioning an image sensor for a different view or positioning a sonar sensor for a different direction of collection, or to a location where different sensors can be accessed, such as moving a collector to connect up to a sensor at a location in an environment by a wired or wireless connection”, also, see [0710]),
a mobile factory unit associated with the data center (see [0346]),
a mobile energy resource associated with the data center,
at least one drone associated with the data center, or at least one vehicle associated with the data center (see [0346] “…drone…”; [0622]).
Hart further teaches wherein the point of consumption that is mobile includes at least one of, a mobile energy resource associated with the data center, (see Fig. 1, 4A so, see [0055] “the power management system 120 includes a generator 420, a set of batteries 421 and optionally a roof mounted solar panels shown generally at 422. The power management system is connected to the processing units via appropriate cables 423, 424 as will be described in more detail below…”)
As per claim 31, Cella-Li-Hart teaches the AI-based platform of claim 29, Li further teaches wherein the location of the point of consumption at a current and/or future time point is determined to orchestrate delivery of at least a portion of the heterogeneous set of energy types to the point of consumption at the location at the current and/or future time point (this has been interpreted as the energy consumed at the location at a current/future time is configured to orchestrate/control the delivery of a portion of energy at the current time or future time, which is inherent; Li teaches see Fig. 31 and see Col 25 lines 30-44 “FIG. 31 illustrates an example of a switch management optimization timeline of iSwitch. The controller re-shuffles the renewable energy powered servers (demand smoothing) at a coarse-grained time interval R (e.g., 15 minutes as the default value in some experiments). During each re-shuffling interval, the average load utilization (lazy tracking) is recorded in a fine-grained time interval (e.g., 1 min) and is used to predict the load for the next period. Upon rescheduling, the optimizer 2906 in the iSwitch scheduler updates the baseline switch operations of each server cluster in SAB 2903 with the goal of mitigating the likelihood of severe load power fluctuation in the next control period. Each switch tuning invoked by CSC 2703 will be assigned based on the updated SAB 2903).
Therefore, it would have been obvious to one of ordinary skilled in the art before effective filing date of the claimed invention to which said subject matter pertains to have modified Cella-Li-Hart’s combination as taught above to include wherein the location of the point of consumption at a current and/or future time point is determined to orchestrate delivery of at least a portion of the heterogeneous set of energy types to the point of consumption at the location at the current and/or future time point as taught by Li in order to provide the energy at the desired or determined time (see Fig. 11 and Fig. 31) with the goal of mitigating the likelihood of severe load power fluctuation in the next control period (see Col 25 lines 40-41).
As to claim 41, this claim is the system claim corresponding to the system claim 29 and is rejected for the same reasons mutatis mutandis.
As to claim 42, this claim is the system claim corresponding to the system claim 30 and is rejected for the same reasons mutatis mutandis.
As to claim 43, this claim is the system claim corresponding to the system claim 31 and is rejected for the same reasons mutatis mutandis.
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.
The prior art made of record and not relied upon, as cited in PTO form 892, is considered pertinent to applicant's disclosure.
Cavness et al (US 20200040272) teaches a system and method comprising a mobile data center and mobile power sources including solar panels, generators, batteries, etc., wherein, the delivery of power of each of these sources is optimized or improved (see 0132).
Hellriegel et al (US 20150130352) teaches a mobile data center and mobile power sources including batteries, etc., wherein, the delivery of power of each of these sources is optimized or improved (0024, 0061, claim 11).
McGrane et al (US 20080178019) teaches a system managing the delivery of power in a data center, and to allocate and associate priorities to workloads for power allocation and budget (see abstract and 0033).
Miller (US 20060276938) teaches a system for optimizing the delivery of renewable energy to a point of consumption, where the point of consumption is associated with one or more loads/appliances (see Figs. 1, 3, and 9 and see Abstract).
Examiner respectfully requests, in response to this Office action, support be shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line number(s) in the specification and/or drawing figure(s). This will assist Examiner in prosecuting the application.
When responding to this Office Action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. Applicant must also show how the amendments avoid or differentiate from such references or objections. See 37 CFR 1.111 (c).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLVIN LOPEZ ALVAREZ whose telephone number is (571) 270-7686 and fax (571) 270-8686. The examiner can normally be reached Monday thru Friday from 9:00 A.M. to 6:00 P.M.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Robert Fennema, can be reached at (571) 272-2748. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/O. L./
Examiner, Art Unit 2117
/DARRIN D DUNN/Patent Examiner, Art Unit 2117
Prosecution Insights