Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/03/2026 has been entered.
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) 1-10, 14, 15, 17, 18, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Worrall et al. (US PG PUB 20190163152), hereinafter "Worrall". in views of Zhou et al. (US PG PUB 20140324495), hereinafter "Zhou".
Regarding Claim 1, Worrall discloses:
An artificial-intelligence-based (AI-based) platform for enabling intelligent orchestration and management of power and energy (i.e. machine learning [i.e. AI-based] system for sharing/managing power energy in a mesh energy system) (Fig. 1, Fig. 2A and ¶ 0004 – 0005, ¶ 0033 and ¶ 0051), the AI-based platform comprising:
a set of adaptive, autonomous data handling systems (i.e. a set of computing systems 204 [i.e. adaptive autonomous data handling systems] associated with a plurality of grid participants) (204 - Fig. 2, ¶ 0028 and ¶ 0033),
wherein each of the adaptive, autonomous data handling systems is configured to collect data relating to at least one of energy generation, energy storage, energy delivery, or energy consumption (i.e. a computing system 204 / an agent [i.e. each of the adaptive autonomous data handling system] may monitor [i.e. collect data] and forecast energy consumption by a grid participant associated with the agent) (204, 206 & 208 – Fig. 2 and ¶ 0032 – 0033),
wherein the data is collected from a set of edge devices (i.e. the data from energy component 208 may be obtained [i.e. collected] through a set of regulator 206 [i.e. a set of edge devices]) (204, 206 & 208 – Fig. 2 and ¶ 0032)
that are in operational control of a set of distributed energy resources and a set of legacy grid-based energy resources (i.e. regulators 206 may be effective to control both inflow and outflow by adjusting current, voltage or both [i.e. operational control] associated with energy component 208 which may comprise one or more of an energy generators 104a and 104b such as renewable sources of energy such as solar photovoltaic panels, wind turbines, etc. and/or battery storage [i.e. a set of distributed energy resources] as well as non-renewable sources of energy such as diesel generators [i.e. a set of legacy grid-based energy resources] of microgrids 102) (102 – Fig. 1, 208 - Fig. 2, Fig. 6, ¶ 0024, ¶ 0029, ¶ 0032 and ¶ 0048 - 0049);
an adaptive energy data pipeline configured to receive collected data from the autonomous data handling systems and communicate the received data across a set of nodes in a network (i.e. data connection [i.e. data pipeline] between the computing system / agent and network connection 202 [i.e. an adaptive energy data pipeline] may receive data collected by computing systems 204 / agents [i.e. the autonomous data handling systems] and transmit the data over network 220 to other agents of the other grid participants [i.e. across a set of nodes in a network]) (202, 204 & 220 – Fig. 2, 710 – Fig. 7, ¶ 0028, ¶ 0031 and ¶ 0054 - 0055); and
a machine-learning system configured to generate a set of predictions related to operation of a plurality of energy resources of a power grid (i.e. Agents may further comprise at least one signal processor, a memory effective to store a database of historical grid participant usage and/or output, and a forecast generator 308/318, effective to forecast [i.e. generate predictions] future energy needs, future energy production, etc. [i.e. data related to operation of a plurality of energy resources of a power grid] based on historical power consumption data, machine learning [i.e. a machine-learning system], and/or reference to external resources) (308 & 318 – Fig. 3, ¶ 0033, ¶ 0036 and ¶ 0046 - 0047).
However, Worral does not explicitly disclose:
wherein the AI-based platform is configured to, determine whether the set of predictions includes a maintenance prediction that is associated with a change in at least one operational parameters of a set of energy-related infrastructure related to at least one of the set of distributed energy resources or the set of legacy grid-based energy resources; wherein the change in the at least one operational parameter is based on a comparison of, a first measurement of the at least one operational parameter by at least one sensor at a first time, and a second measurement of the at least one operational parameter by the at least one sensor at a second time; and in response to determining that the set of predictions includes the maintenance prediction, scheduling a set of maintenance operations at a set of times.
On the other hand, in the same field of endeavor, Zhou teaches:
a machine-learning system configured to generate a set of predictions related to operation of a plurality of energy resources of a power grid (i.e. neural network based system [i.e. a machine-learning system] may generate multiple factors [i.e. a set of predictions], e.g. probability of failure / predicted failure risk 705, energy price forecast and wind power forecast, for wind turbines [i.e. a plurality of energy resources] attached to a power grid) (Abstract, Fig. 2, Fig. 7, ¶ 0022, ¶ 0044 and ¶ 0048)
wherein the AI-based platform (i.e. the neural network based system [i.e. the AI-based platform]) (Fig. 2, ¶ 0022, ¶ 0044 and ¶ 0047) is configured to,
determine whether the set of predictions includes a maintenance prediction that is associated with a change in at least one operational parameters of a set of energy-related infrastructure related to at least one of the set of distributed energy resources or the set of legacy grid-based energy resources (i.e. the system [i.e. the AI-based platform] may determine whether the input/maintenance factors [i.e. the set of predictions] include predicted failure risk / probability of failure that are above risk threshold [i.e. a maintenance prediction], wherein the predicted failure risk / probability of failure are associated with components of wind turbines [i.e. a set of energy-related infrastructure related to the set of distributed energy resources]; For example, the temperature [i.e. the at least one operational parameter] of a component may vary [i.e. a change in at least one operational parameters of a set of energy-related infrastructure] with wind speed in components that fail but not in components that do not fail) (360, 365 & 370 – Fig. 3, ¶ 0022, ¶ 0026 - 0027 ¶ 0040 and ¶ 0048 – 0049);
wherein the change in the at least one operational parameter is based on a comparison of, a first measurement of the at least one operational parameter by at least one sensor at a first time, and a second measurement of the at least one operational parameter by the at least one sensor at a second time (i.e. the risk modeling module may monitor data from sensors [i.e. a first measurement of the at least one operational parameter by at least one sensor and a second measurement of the at least one operational parameter by the at least one sensor at a second time] in the wind turbines in the wind farm. The sensors may measure the power output of a particular component, the vibrations in a component, temperature of a component; For example, the temperature [i.e. the change in the at least one operational parameter] of a component may vary [i.e. comparison of a first measurement and a second measurement] with wind speed in components that fail but not in components that do not fail) (¶ 0026 - 0027 and ¶ 0033 - 0034) and
in response to determining that the set of predictions includes the maintenance prediction, scheduling a set of maintenance operations at a set of times (i.e. in response to determining that the input/maintenance factors [i.e. the set of predictions] include predicted failure risk / probability of failure that are above risk threshold [i.e. the maintenance prediction], the system may schedule a set of maintenance tasks [i.e. a set of maintenance operations] on a set of dates [i.e. a set of times]) (754 - Fig. 7, ¶ 0048 and ¶ 0053 - 0054).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the method/system/computer-readable-medium of Worrall to include the feature wherein the AI-based platform is configured to, determine whether the set of predictions includes a maintenance prediction that is associated with a change in at least one operational parameters of a set of energy-related infrastructure related to at least one of the set of distributed energy resources or the set of legacy grid-based energy resources; wherein the change in the at least one operational parameter is based on a comparison of, a first measurement of the at least one operational parameter by at least one sensor at a first time, and a second measurement of the at least one operational parameter by the at least one sensor at a second time; and in response to determining that the set of predictions includes the maintenance prediction, scheduling a set of maintenance operations at a set of times as taught by Zhou so that preventative maintenance may be performed for the energy resources thereby maximizing the energy generation (Abstract).
Regarding Claim 2, Worral and Zhou disclose, in particular Worral teaches:
wherein the set of legacy grid-based energy resources includes at least one of a generation asset, a transmission asset, a substation asset, or a distribution asset (i.e. non-renewable sources of energy of microgrids 102 [i.e. the set of legacy grid-based energy resources] may include diesel generators [i.e. a generation asset]) (102 – Fig. 1, 208 - Fig. 2, ¶ 0048 - 0049).
Regarding Claim 3, Worral and Zhou disclose, in particular Worral teaches:
wherein the adaptive energy data pipeline is further configured to adapt a transport of data over at least one of a network or a communication system (i.e. Network connection 202 [i.e. the adaptive energy data pipeline] may allow grid participant 200 to send and receive data [i.e. configured to adapt a transport of data] to one or more other computing devices and/or grid participants over a network [i.e. at least one of a network or a communication system]) (202 – Fig. 2 and ¶ 0031),
wherein the adapting is based on at least one of a congestion condition, a delay and/or latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality-of-service (QoS) condition, a usage condition, a market factor condition, and a user configuration condition (i.e. one or more other computing devices and/or grid participants may be able to remotely provide instructions to computing system 204 through network connection 202 in order to control various operations of computing system 204 and/or in order to remotely control operation of one or more other components of grid participant 200 by interfacing with computing system 204 [i.e. the adapting is based on a user configuration condition]) (¶ 0031).
Regarding Claim 4, Worral and Zhou disclose, in particular Worral teaches:
wherein each of the adaptive, autonomous data handling systems is further configured to adapt a transport of data over at least one of a network or a communication system (i.e. computing system 204 [i.e. each of the adaptive, autonomous data handling systems] may allow grid participant 200 to send and receive data [i.e. configured to adapt a transport of data] to one or more other computing devices and/or grid participants over a network [i.e. at least one of a network or a communication system]) (202 & 204 – Fig. 2 and ¶ 0030 - 0031),
wherein the adapting is based on at least one of a congestion condition, a delay condition, a latency condition, a packet loss condition, an error rate condition, a cost of transport condition, a quality-of-service (QoS) condition, a usage condition, a market factor condition, or a user configuration condition (i.e. one or more other computing devices and/or grid participants may be able to remotely provide instructions to computing system 204 through network connection 202 in order to control various operations of computing system 204 and/or in order to remotely control operation of one or more other components of grid participant 200 by interfacing with computing system 204 [i.e. the adapting is based on a user configuration condition]) (¶ 0031).
Regarding Claim 5, Worral and Zhou disclose, in particular Worral teaches:
further comprising at least one AI-based model and/or algorithm, wherein the at least one AI-based model and/or algorithm is trained based on a training data set, and the training data set is based on at least one of at least one human tag, at least one label, at least one human interaction with a hardware system, at least one human interaction with a software system, at least one outcome, at least one AI-generated training data sample, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process (i.e. forecast generator [i.e. AI-based model and/or algorithm] in a machine learning environment [i.e. trained based on a training data set]; a forecast generator of an agent for a solar photovoltaic system may use historical data for the given month of the year, combined with input from one or more irradiance sensors [i.e. at least one outcome] and input from an external weather service to forecast an amount of energy that will be produced over the next time period) (¶ 0051).
Regarding Claim 6, Worral and Zhou disclose, in particular Worral teaches:
wherein at least one node of the set of nodes is configured to orchestrate a delivery of energy to at least one point of consumption (i.e. second grid participant [i.e. at least one node of the set of nodes] may change load profile to discharge energy storage, increase energy generation, etc. in order to supply energy [i.e. orchestrate a delivery of energy] requested by the first grid participant [i.e. at least one point of consumption]) (710 & 750 – Fig. 7, ¶ 0054 – 0055 and ¶ 0058), and
the delivery of the energy includes at least one of at least one fixed transmission line, at least one instance of wireless energy transmission, at least one delivery of fuel, or at least one delivery of stored energy (i.e. the energy may be delivered to the first grid participant by discharging energy storage [i.e. the delivery of the energy includes at least one delivery of stored energy]) (750 – Fig. 7 and ¶ 0058).
Regarding Claim 7, Worral and Zhou disclose, in particular Worral teaches:
wherein at least one node of the set of nodes is further configured to record, in a distributed ledger and/or blockchain, 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 (i.e. Agents [i.e. at least one node of the set of nodes] may further comprise at least one signal processor, a memory effective to store a database of historical grid participant usage and/or output, and a forecast generator, effective to forecast future energy needs based on historical power consumption data [i.e. an energy consumption event]; wherein the database may be a distributed database [i.e. distributed ledger]) (¶ 0033 and ¶ 0092).
Regarding Claim 8, Worral disclose:
wherein at least one node of the set of nodes 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 (i.e. micro-grid participants [i.e. at least one node of the set of nodes] may be deployed in an environment with a variety of energy generations system, e.g. diesel generator, photovoltaic panels, battery banks, etc. [i.e. the off-grid environment includes at least one of an off-grid energy generation system, an off-grid energy storage system]) (¶ 0048 – 0049).
Regarding Claim 9, Worral and Zhou disclose, in particular Worral teaches:
wherein the adaptive energy data pipeline is further configured to monitor at least one of, an overall energy consumption by at least a portion of the set of nodes, or a role of at least one node of the set of nodes in an overall energy consumption by at least a portion of the set of nodes (i.e. agent may monitor, by communicating with energy meter 642/652, via network connection 202 [i.e. the adaptive energy data pipeline] relative contribution of various loads [i.e. an overall energy consumption by at least a portion of the set of nodes] and may produce a forecast of energy usage in the aggregate) (¶ 0041 and ¶ 0049), and
based on the monitoring, perform at least one of, managing an energy consumption by the set of nodes, forecasting an energy consumption by the set of nodes, or provisioning resources associated with energy consumption by the set of nodes (i.e. Agent 404 may monitor and record energy information with enough detail to determine the relative contribution of various loads and may produce a forecast of energy usage in the aggregate [i.e. forecasting an energy consumption by the set of nodes]) (¶ 0041 and ¶ 0049).
Regarding Claim 10, Worral and Zhou disclose, in particular Worral teaches:
wherein the set of nodes in the network include a set of edge networking devices that govern at least one of energy generation, energy storage, energy delivery, or energy consumption by a set of operating devices that are controlled via the set of edge networking devices (i.e. micro-grid participants / agents [i.e. the set of nodes in the network that comprise the adaptive energy data pipeline] may comprise battery management systems [i.e. a set of edge networking devices] that controls/governs storage and dissemination of power as needed by local loads [i.e. at least one of energy generation, energy storage, energy delivery]) (Fig. 6 and ¶ 0049).
Regarding Claim 14, Worral and Zhou disclose, in particular Worral teaches:
wherein the adaptive energy data pipeline includes a self- organizing data storage, the self- organizing data storage being configured to store data on a device based on at least one of a pattern of the data, content of the data, or context of the data (i.e. Agents may further comprise at least one signal processor, a memory effective to store a database [i.e. a self- organizing data storage] of historical grid participant usage and/or output [i.e. store data on a device based on content of the data], and a forecast generator, effective to forecast future energy needs based on historical power consumption data, machine learning, and/or reference to external resources) (¶ 0033).
Regarding Claim 15, Worral and Zhou disclose, in particular Worral teaches:
wherein the adaptive energy data pipeline is configured to perform automated, adaptive networking, the automated adaptive networking includes at least one of adaptive protocol selection, adaptive routing of data based on RF conditions, adaptive filtering of data, adaptive slicing of network bandwidth, adaptive use of cognitive network capacity, or adaptive use of peer-to-peer network capacity (i.e. the computing system 204 having the data connection with network connection 202 [i.e. the adaptive energy data pipeline] may perform the adaptation of the communication protocols [i.e. automated, adaptive networking]; For example, the system may maintain attributes, such as supported protocols, of the various sharing partners / grid participants and select a protocol [i.e. the adaptive networking including adaptive protocol selection] that is compatible and compliant with the grid participants on the participant list for energy data broadcasting) (¶ 0021 and ¶ 0068 - 0069).
Regarding Claim 17, Worral and Zhou disclose, in particular Worral teaches:
wherein at least one node of the set of nodes is further configured to adjust communication with at least one other node of the set of nodes to adapt a reporting, to the at least one other node, of data associated with the at least one of energy generation, energy storage, energy delivery, or energy consumption (i.e. agents / grid participants [i.e. at least one node of the set of nodes] may be effective to translate data [i.e. adjust communication] from one standard and/or protocol into another [i.e. adapt a reporting] and rebroadcast the translated signal to other grid participants in order to effectively disseminate power data [i.e. data associated with the at least one of energy generation, energy storage, energy delivery, or energy consumption] within the mesh-network topology) (¶0034).
Regarding Claim 18, Worral and Zhou disclose, in particular Worral teaches:
wherein the at least one node of the set of nodes is further configured to adapt reported data to at least one other node of the set of nodes, wherein adapting the reported data is based on a priority of a consumption of the reported data (i.e. agent [i.e. the at least one node of the set of nodes] may prioritize sending of the signal [i.e. reported data] to grid participants; the sending of the signal [i.e. the reported data] is performed according to prioritized list of the grid participants [i.e. ]) (¶ 0072 and ¶ 0075).
Regarding Claim 19, Worral discloses:
A method of enabling intelligent orchestration and management of power and energy via an AI-based platform (i.e. machine learning [i.e. AI] based method for sharing/managing power energy in a mesh energy system) (Fig. 1, Fig. 2A and ¶ 0004 – 0005, ¶ 0033 and ¶ 0051), the method comprising:
collecting, by a set of adaptive, autonomous data handling systems, data relating to at least one of energy generation, energy storage, energy delivery, or energy consumption (i.e. a computing system 204 / an agent [i.e. each of the adaptive autonomous data handling system] may monitor [i.e. collect data] and forecast energy consumption by a grid participant associated with the agent) (204, 206 & 208 – Fig. 2 and ¶ 0032 – 0033),
wherein the data is collected a set of edge devices (i.e. the data from energy component 208 may be obtained [i.e. collected] through a set of regulator 206 [i.e. a set of edge devices]) (204, 206 & 208 – Fig. 2 and ¶ 0032)
that are in operational control of a set of distributed energy resources and a set of legacy grid-based energy resources (i.e. regulators 206 may be effective to control both inflow and outflow by adjusting current, voltage or both [i.e. operational control] associated with energy component 208 which may comprise one or more of an energy generators 104a and 104b such as renewable sources of energy such as solar photovoltaic panels, wind turbines, etc. and/or battery storage [i.e. a set of distributed energy resources] as well as non-renewable sources of energy such as diesel generators [i.e. a set of legacy grid-based energy resources] of microgrids 102) (102 – Fig. 1, 208 - Fig. 2, Fig. 6, ¶ 0024, ¶ 0029, ¶ 0032 and ¶ 0048 - 0049);
receiving, by an adaptive energy data pipeline, collected data from the autonomous data handling systems and communicate the received data across a set of nodes in a network (i.e. data connection [i.e. data pipeline] between the computing system / agent and network connection 202 [i.e. an adaptive energy data pipeline] may receive data collected by computing systems 204 / agents [i.e. the autonomous data handling systems] and transmit the data over network 220 to other agents of the other grid participants [i.e. across a set of nodes in a network]) (202, 204 & 220 – Fig. 2, 710 – Fig. 7, ¶ 0028, ¶ 0031 and ¶ 0054 - 0055); and
generating, by a machine-learning system, a set of predictions related to operation of a plurality of energy resources of a power grid (i.e. Agents may further comprise at least one signal processor, a memory effective to store a database of historical grid participant usage and/or output, and a forecast generator 308/318, effective to forecast [i.e. generate predictions] future energy needs, future energy production, etc. [i.e. data related to operation of a plurality of energy resources of a power grid] based on historical power consumption data, machine learning [i.e. a machine-learning system], and/or reference to external resources) (308 & 318 – Fig. 3, ¶ 0033, ¶ 0036 and ¶ 0046 - 0047).
However, Worral does not explicitly disclose:
determining whether the set of predictions includes a maintenance prediction that is associated with a change in at least one operational parameters of a set of energy-related infrastructure related to at least one of the set of distributed energy resources or the set of legacy grid-based energy resources; wherein the change in the at least one operational parameter is based on a comparison of, a first measurement of the at least one operational parameter by at least one sensor at a first time, and a second measurement of the at least one operational parameter by the at least one sensor at a second time; and in response to determining that the set of predictions includes the maintenance prediction, scheduling a set of maintenance operations at a set of times.
On the other hand, in the same field of endeavor, Zhou teaches:
determining whether the set of predictions includes a maintenance prediction that is associated with a change in at least one operational parameters of a set of energy-related infrastructure related to at least one of the set of distributed energy resources or the set of legacy grid-based energy resources (i.e. the system [i.e. the AI-based platform] may determine whether the input/maintenance factors [i.e. the set of predictions] include predicted failure risk / probability of failure that are above risk threshold [i.e. a maintenance prediction], wherein the predicted failure risk / probability of failure are associated with components of wind turbines [i.e. a set of energy-related infrastructure related to the set of distributed energy resources]; For example, the temperature [i.e. the at least one operational parameter] of a component may vary [i.e. a change in at least one operational parameters of a set of energy-related infrastructure] with wind speed in components that fail but not in components that do not fail) (360, 365 & 370 – Fig. 3, ¶ 0022, ¶ 0026 - 0027 ¶ 0040 and ¶ 0048 – 0049);
wherein the change in the at least one operational parameter is based on a comparison of, a first measurement of the at least one operational parameter by at least one sensor at a first time, and a second measurement of the at least one operational parameter by the at least one sensor at a second time (i.e. the risk modeling module may monitor data from sensors [i.e. a first measurement of the at least one operational parameter by at least one sensor and a second measurement of the at least one operational parameter by the at least one sensor at a second time] in the wind turbines in the wind farm. The sensors may measure the power output of a particular component, the vibrations in a component, temperature of a component; For example, the temperature [i.e. the change in the at least one operational parameter] of a component may vary [i.e. comparison of a first measurement and a second measurement] with wind speed in components that fail but not in components that do not fail) (¶ 0026 - 0027 and ¶ 0033 - 0034) and
in response to determining that the set of predictions includes the maintenance prediction, scheduling a set of maintenance operations at a set of times (i.e. in response to determining that the input/maintenance factors [i.e. the set of predictions] include predicted failure risk / probability of failure that are above risk threshold [i.e. the maintenance prediction], the system may schedule a set of maintenance tasks [i.e. a set of maintenance operations] on a set of dates [i.e. a set of times]) (754 - Fig. 7, ¶ 0048 and ¶ 0053 - 0054).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to modify the method/system/computer-readable-medium of Worrall to include the feature for determining whether the set of predictions includes a maintenance prediction that is associated with a change in at least one operational parameters of a set of energy-related infrastructure related to at least one of the set of distributed energy resources or the set of legacy grid-based energy resources; wherein the change in the at least one operational parameter is based on a comparison of, a first measurement of the at least one operational parameter by at least one sensor at a first time, and a second measurement of the at least one operational parameter by the at least one sensor at a second time; and in response to determining that the set of predictions includes the maintenance prediction, scheduling a set of maintenance operations at a set of times as taught by Zhou so that preventative maintenance may be performed for the energy resources thereby maximizing the energy generation (Abstract).
Regarding Claim 20, Worral and Zhou disclose, in particular Worral teaches:
wherein the set of legacy grid-based energy resources includes at least one of a generation asset, a transmission asset, a substation asset, or a distribution asset (i.e. non-renewable sources of energy of microgrids 102 [i.e. the set of legacy grid-based energy resources] may include diesel generators [i.e. a generation asset]) (102 – Fig. 1, 208 - Fig. 2, ¶ 0048 - 0049).
Claim(s) 11 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Worral in views of Zhou as applied to claim 1 above, and further in view of Dharmadhikari et al. (US PG PUB 20160226757 ), hereinafter "Dharmadhikari".
Regarding Claim 11, Worral and Zhou disclose all the features with respect to Claim 1 as described above.
However, the combination of Worral and Zhou does not explicitly disclose:
wherein the adaptive energy data pipeline is further configured to automatically select a least-cost route for data communicated across the set of nodes, the selection being based on a low-priority energy use related to the data.
On the other hand, in the same field of endeavor, Dharmadhikari teaches:
wherein the adaptive energy data pipeline is further configured to automatically select a least-cost route for data communicated across the set of nodes, the selection being based on a low-priority energy use related to the data (i.e. the system may automatically select lower energy cost route for data communication based on the fact that a particular area’s electricity need and rate are lower [i.e. low-priority energy use]; For example, at 6 am eastern time, the data will be routed through California, instead of New York, because California is still sleeping at that time and has lower electricity rate) (Abstract, ¶ 0039 and ¶ 0085).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method/system of Worral and Zhou to include the feature wherein the adaptive energy data pipeline is further configured to automatically select a least-cost route for data communicated across the set of nodes, the selection being based on a low-priority energy use related to the data as taught by Dharmadhikari so that data traffic may be routed to a destination based on energy cost (Abstract and ¶ 0039).
Regarding Claim 13, Worral and Zhou disclose all the features with respect to Claim 1 as described above.
However, the combination of Worral and Zhou does not explicitly disclose:
wherein the adaptive energy data pipeline includes a set of artificial intelligence capabilities, the capabilities being configured to adapt the pipeline to enable regulation of elements of data transmission in coordination with energy orchestration needs.
On the other hand, in the same field of endeavor, Dharmadhikari teaches:
wherein the adaptive energy data pipeline includes a set of artificial intelligence capabilities, the capabilities being configured to adapt the pipeline to enable regulation of elements of data transmission in coordination with energy orchestration needs (i.e. data processing module and profile module [i.e. a set of artificial intelligence capabilities] may determine to route data traffic [i.e. regulation of elements of data transmission] based on likelihood of energy needs [i.e. energy orchestration needs]) (¶ 0039, ¶ 0051 and ¶ 0085).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method/system of Worral and Zhou to include the feature wherein the adaptive energy data pipeline includes a set of artificial intelligence capabilities, the capabilities being configured to adapt the pipeline to enable regulation of elements of data transmission in coordination with energy orchestration needs as taught by Dharmadhikari so that data traffic may be routed to a destination based on energy cost (Abstract and ¶ 0039).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Worral in views of Zhou as applied to claim 1 above, and further in view of DeCusatis et al. (US PG PUB 20140189157), hereinafter "DeCusatis".
Regarding Claim 12, Worral and Zhou disclose all the features with respect to Claim 1 as described above.
However, the combination of Worral and Zhou does not explicitly disclose:
wherein the adaptive energy data pipeline is further configured to automatically select a high-quality of service route for data communicated across the set of nodes, the selection being based on a high-priority energy use related to the data.
On the other hand, in the same field of endeavor, DeCusatis teaches:
wherein the adaptive energy data pipeline is further configured to automatically select a high-quality of service route for data communicated across the set of nodes, the selection being based on a high-priority energy use related to the data (i.e. the method/system may automatically select low latency routes [i.e. high-quality service route] for data flow [i.e. data communicated across the set of nodes] base on priority of the routes determined with respect to their respective energy consumptions [i.e. a high-priority energy use related to the data]) (¶ 0045 - 0046).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method/system of Worral and Zhou to include the feature wherein the adaptive energy data pipeline is further configured to automatically select a high-quality of service route for data communicated across the set of nodes, the selection being based on a high-priority energy use related to the data as taught by DeCusatis so that data flows may be routed based on the route’s energy consumption as well as quality of service requirements (¶ 0046).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Worral in views of Zhou as applied to claim 1 above, and further in view of Goparaju et al. (US PG PUB 20150301548), hereinafter "Goparaju".
Regarding Claim 16, Worral and Zhou disclose all the features with respect to Claim 1 as described above.
However, the combination of Worral and Zhou does not explicitly disclose:
wherein the adaptive energy data pipeline is configured to perform enterprise contextual adaptation by automatically processing data based on at least one of an operating context of an enterprise, a transactional context of an enterprise, or a financial context of an enterprise.
On the other hand, in the same field of endeavor, Goparaju teaches:
wherein the adaptive energy data pipeline is configured to perform enterprise contextual adaptation by automatically processing data based on at least one of an operating context of an enterprise, a transactional context of an enterprise, or a financial context of an enterprise (i.e. the method/system may dynamically manage energy consumption of an enterprise, by adapting dynamic situations [i.e. perform enterprise contextual adaptation by automatically processing data], based on processing of the historical information related to energy consumption and changes in equipment settings [i.e. an operating context of an enterprise]) (Abstract, ¶ 0016 and ¶ 0039 - 0043).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method/system of Worral and Zhou to include the feature wherein the adaptive energy data pipeline is configured to perform enterprise contextual adaptation by automatically processing data based on at least one of an operating context of an enterprise, a transactional context of an enterprise, or a financial context of an enterprise as taught by Goparaju so that enterprise’s energy needs may be managed based on the historical information related to energy consumption of an enterprise (Abstract, ¶ 0016 and ¶ 0039 - 0043).
Claim(s) 21-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Worral in views of Zhou as applied to claims 1 and 19 above, and further in view of Zhou et al. (US PG PUB 20140316838), hereinafter "Zhou2".
Regarding Claim 21, Worral and Zhou disclose all the features with respect to Claim 19 as described above.
In addition, Worral and Zhou, in particular Zhou teaches:
the set of predictions includes the maintenance prediction (i.e. the input/maintenance factors [i.e. the set of predictions] include predicted failure risk / probability of failure that are above risk threshold [i.e. maintenance prediction]) (360, 365 & 370 – Fig. 3, ¶ 0022, ¶ 0040 and ¶ 0048 – 0049), and
the maintenance prediction includes an indication of at least one of, a leak, shaking, system stress, or a defect (i.e. Each component may be associated with a different probability of failure [i.e. maintenance prediction] that may be based on the current wind conditions, vibrations [i.e. an indication of shaking] in the component, age of the component, and the like) (¶ 0023).
However, the combination of Worral and Zhou does not explicitly disclose:
wherein, the set of times is based on a prediction associated with load forecasting.
On the other hand, in the same field of endeavor, Zhou2 teaches:
wherein, the set of times is based on a prediction associated with load forecasting (i.e. proposed maintenance schedule [i.e. the set of times] is based on power plant relevant parameter (PF) which may include predicted/forecasted energy demand [i.e. load forecasting]) (Fig. 6, ¶ 0016 and ¶ 0031).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method/system of Worral and Zhou to include the feature wherein, the set of times is based on a prediction associated with load forecasting as taught by Zhou2 in order to minimize revenue loss (Fig. 6, ¶ 0016 and ¶ 0037).
Regarding Claim 22, Worral and Zhou disclose all the features with respect to Claim 1 as described above.
However, the combination of Worral and Zhou does not explicitly disclose:
wherein, the set of times is based on a prediction associated with load forecasting.
On the other hand, in the same field of endeavor, Zhou2 teaches:
wherein, the set of times is based on a prediction associated with load forecasting (i.e. proposed maintenance schedule [i.e. the set of times] is based on power plant relevant parameter (PF) which may include predicted/forecasted energy demand [i.e. load forecasting]) (Fig. 6, ¶ 0016 and ¶ 0031).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method/system of Worral and Zhou to include the feature wherein, the set of times is based on a prediction associated with load forecasting as taught by Zhou2 in order to minimize revenue loss (Fig. 6, ¶ 0016 and ¶ 0037).
Regarding Claim 23, Worral, Zhou and Zhou2 disclose, in particular Zhou teaches:
the set of predictions includes the maintenance prediction (i.e. the input/maintenance factors [i.e. the set of predictions] include predicted failure risk / probability of failure that are above risk threshold [i.e. maintenance prediction]) (360, 365 & 370 – Fig. 3, ¶ 0022, ¶ 0040 and ¶ 0048 – 0049), and
the maintenance prediction includes an indication of at least one of, a leak, shaking, system stress, or a defect (i.e. Each component may be associated with a different probability of failure [i.e. maintenance prediction] that may be based on the current wind conditions, vibrations [i.e. an indication of shaking] in the component, age of the component, and the like) (¶ 0023).
The prior art used in the rejection of the current claim is combined using the same motivations as was applied in claim 22.
Conclusion
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/Soe Hlaing/ Primary Examiner, Art Unit 2451
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