DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/14/2025 has been entered.
Response to Arguments
Applicant's arguments filed 10/14/2025 have been fully considered, but they are not fully persuasive. The updated 35 USC § 103 rejection of claims 1-14 are applied in light of Applicant's amendments.
The Applicant argues “Vega and Murai do not teach or suggest "the operation plan server includes a management table in which a user ID of the user, the handling cost, and a power consumption amount of the user in each time block of a plurality of time blocks of a target time period of the operation plan are stored." (Remarks 10/14/2025)
In response, the Examiner respectfully disagrees. The combination of Vega and Murai teach all the limitations. The amendments made by the Applicant are nonfunctional descriptive material and are not functionally involved in the steps recited. The storage of data/management table would be performed the same regardless of the data type. Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability, (see in re Gulack, 217 USPQ 401 (Fed. Cir. 1983). Additionally, Vega teaches a storage device (management table) that implements database tables to organize and store the recited data elements. The art teaches a premise identifier that is able to identify each premises (user ID), that identifies each user that us specifically consuming the power. Lastly, Vega also teaches time periods/blocks and the consumption amount by each user. Thus, one of ordinary skill in the art would agree that the combination of Vega and Murai teach all the limitation of claim 1 (see rejection below for citations from Vega).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1-2, 8-9, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20210123771 (hereinafter “Vega”) et al., in view of U.S. PGPub 20110270459 to (hereinafter “Murai”) et al.
As per claim 1, Vega teaches:
An operation plan creation device in a data center, the data center including a plurality of server devices each configured to execute a workload of a user, workload execution management servers respectively provided in the server devices, and an operation plan server connected to the workload execution management servers,
Vega 0085-0095: “The present disclosure provides an energy analytics and optimization control system for use by an end-user for monitoring and controlling energy consumption, consisting of a processor; a first memory for storing programming instructions for the processor, wherein a first set of programming instructions when executed by the processor…the at least one premises information source may be a database server configured to collect and provision the at least one at least one premises information… the at least one lifestyle information source may be a database server configured to collect and provision the at least one at least one lifestyle information.”
wherein each of the workload execution management servers manages execution of the workload of the user and includes a cost calculation unit configured to calculate a handling cost to paid by a data center operator to the user to handle a consumption pattern of the user, Vega 0208: then a calculation and comparison of average hourly electricity usage during idle and away periods may be made, followed by an estimation of the resulting associated cost and emissions 7322 to the environment. The results from these calculation and comparison steps may then be displayed 7324 as descriptive analytics in formats such as for example, but not limited to idle and away electricity consumption and associated energy leakage, cost and emissions over a period of time (e.g., annual, monthly, weekly, etc.). These steps are briefly summarized herein below regarding FIG. 21…0288: a customer is provided with time series monitoring of actual versus forecasted energy usage 13020 with trending capabilities and the quantified changes in key energy indicators (including costs, efficiency, energy leakage, and environmental footprint) during a given period via an energy dashboard that is updated using new energy consumption…0343: Machine learning algorithms continuously learn new patterns and update existing patterns from the users refining suggestions and actions to improve energy consumption with minimal discomfort (FIG. 41 depicts one representative example of a machine learning method to accomplish this action).”
and the operation plan server includes a cooperative operation plan optimization problem creation unit configured to create an optimization problem for minimizing the handling cost when adjusting the consumption pattern of the user,Vega 0085: determination of unintended energy consumption, efficiency of energy consumption calculated energy optimization score (e.g., Energy IQ), comparisons of energy usage for similar reference premises at the preselected locations for preselected time periods, baseline energy consumption including breakdowns for devices, periodic comparisons of baseline to actual consumption, listing of variances between baseline and actual usage, recommendations for correcting variances, recommendations for corrections of variances to reduce energy consumption to correct variances and reduce consumption, recommendations for adjustment in preference and schedule data for a user to control and reduce energy consumption and environmental impact to correct variances and reduce consumption…0356: calculated energy optimization score (e.g. Energy IQ), comparisons of energy usage for similar reference premises at the preselected locations for preselected time periods, baseline energy consumption including breakdowns for devices, periodic comparisons of baseline to actual consumption, listing of variances between baseline and actual usage, recommendations for correcting variances, recommendations for corrections of variances to reduce energy consumption to correct variances and reduce consumption, recommendations for adjustment in preference and schedule data for a user to control and reduce energy consumption, cost, and environmental impact to correct variances and reduce consumption
an optimization equation update unit configured to update an operation plan optimization equation such that a consumption pattern obtained by changing a consumption pattern in a past response history of the user according to a certain rule is available at a handling cost to handle the changed consumption pattern, Vega 0084-0085: analyzing said data for energy consumption by one or more energy devices associated with said premises, (vii) computation of energy costs using said converted and stored historical energy usage data…cause the processor to partition historical data, aggregate, compare and analyze said data using at least common time period and time slice information for each premises of the plurality of premises; a second memory for separately storing the preselected data from multiple sources that comprises historical energy usage data for preselected locations for the premises… determination of unintended energy consumption, efficiency of energy consumption calculated energy optimization score (e.g., Energy IQ), comparisons of energy usage for similar reference premises at the preselected locations for preselected time periods, baseline energy consumption including breakdowns for devices, periodic comparisons of baseline to actual consumption, listing of variances between baseline and actual usage, recommendations for correcting variances, recommendations for corrections of variances to reduce energy consumption to correct variances and reduce consumption, recommendations for adjustment in preference and schedule data for a user to control and reduce energy consumption and environmental impact to correct variances and reduce consumption..0328: Display and Side by side comparison of top optimization recommendations identified based on weighted scores calculated based on the customer's optimization criteria [0329] Graphical comparison of the current condition and recommended optimization projected costs based on baseline historical and predicted consumption [0330] Comparison of environmental impact of the current and recommended condition and recommended optimization projected costs based on baseline historical and predicted consumption [0331] Interface to actuate the optimization recommendations [0332] Interface to set the actuation mode for the optimization recommendations [0333] (e.g. manual, automatic, hybrid), and to define automation settings, notifications and thresholds.”
and an operation plan draft calculation unit configured to create, by obtaining a solution of the updated operation plan optimization equation, an operation plan draft for the consumption pattern of the user and a device held by a data center operator;Vega 0254: “Following this additional data input, the method may next generate personalized and customizable actionable information 1380 such as, for example, but not limited to visualization of time-series usage data for comparison against localized and regional locations (benchmarking), unique energy usage breakdown by appliances (interior and exterior), unique energy efficiency indicators for cost savings, energy reduction, and consequently the environmental impact savings by the consumer. This information may then be used to calculate a base line with descriptive and predictive analytics and use that as a basis for determining deviations or variations (using an end-user's criteria) 1390... FIGS. 28-30 provide representative examples of the types of results that are available from the system and methods of the present disclosure after an energy fingerprint result 1380, 1390 has been calculated and determined. Once determined the results of the method (energy fingerprint) may then be further used to obtain and analyze energy consumption to establish a baseline for energy consumption and monitoring actual consumption for variances from the baseline and the determination of the cause for and correction of a variance.”
the operation plan server includes a management table in which a user ID of the user, the handling cost, and a power consumption amount of the user in each time block of a plurality of time blocks of a target time period of the operation plan are stored;Vega, claims 1, 6, 9, and 19: “and the at least one actuation data based on the analyzing; and a storage device configured for storing each of the at least one utility consumption information, the at least one premises information, the at least one lifestyle information and at least one of the at least one utility recommendation and the at least one actuation data… wherein the at least one efficiency indicator comprises a first efficiency indicator corresponding to a first time period and a second efficiency indicator corresponding to a second time period… wherein the at least one premises information comprises a premises identifier associated with a premises of the at least one premises… performing a fourth comparison of a utility consumption information associated with the premises and the reference utility consumption information; and determining a utility optimization score based on the fourth comparison.” Vega 0208: “then a calculation and comparison of average hourly electricity usage during idle and away periods may be made, followed by an estimation of the resulting associated cost and emissions 7322 to the environment. The results from these calculation and comparison steps may then be displayed 7324 as descriptive analytics in formats such as for example, but not limited to idle and away electricity consumption and associated energy leakage, cost and emissions over a period of time (e.g., annual, monthly, weekly, etc.). These steps are briefly summarized herein below regarding FIG. 21…0288: a customer is provided with time series monitoring of actual versus forecasted energy usage 13020 with trending capabilities and the quantified changes in key energy indicators (including costs, efficiency, energy leakage, and environmental footprint) during a given period via an energy dashboard that is updated using new energy consumption…0343: Machine learning algorithms continuously learn new patterns and update existing patterns from the users refining suggestions and actions to improve energy consumption with minimal discomfort (FIG. 41 depicts one representative example of a machine learning method to accomplish this action).”
Vega may not explicitly teach the following. However, Murai teaches:
the workload management servers adjust the power consumption of the data center to approach a target without disclosing individual workload information of the user to the data center operator;Murai 0046: “FIG. 6 illustrates an example of the change of the power consumption in such an equipment load in the building as the management target in a day when the control is performed according to the request from the power supply manager 10 as described above…claim 6: A power supply manager that is connected through a network to a power consumption manager managing power consumption regarding a load of equipment of each of one or more structures as management targets, the power consumption manager being placed for each of the structures, the power supply manager managing power supply to each of the structures comprising: a management unit that determines whether or not it is necessary to request a change of the power consumption for each of the structures; a request unit that transmits a changeable power amount acquisition request to the power consumption manager when the management unit determines that it is necessary to request the change of the power consumption for the structure; and a notification unit that acquires changeable power amount information created in the power consumption manager based on the changeable power amount acquisition request transmitted from the request unit, the changeable power amount information being with respect to a planned value of the power consumption regarding the load of each piece of equipment in the structure as a monitoring target in a preset predetermined period in future, decides a power change request amount for each of the structures based on the acquired changeable power amount information, and transmits the decided power change request amount to each of the power consumption managers for a purpose of notification.”
Vega and Murai are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Vega with the aforementioned teachings from Murai with a reasonable expectation of success, by adding steps that allow the software to adjust consumption with the motivation to more accurately analyze and implement [Ellice 0145].
As per claim 2, Vega and Murai teach all the limitation of claim 1.
In addition, Vega teaches:
wherein the operation plan server further includes a job queue calculation unit configured to compare the consumption pattern of the user with a corresponding consumption pattern base line of the user registered in advance, and calculate, in each of the consumption patterns, a lower limit of a power consumption corresponding to an amount of an execution standby job queue in each time block, and every time the optimization equation update unit receives a consumption pattern and a handling cost from the user, the optimization equation update unit updates the operation plan optimization equation such that a pattern that is achievable by advancing execution of a job queue calculated based on the consumption pattern is available as an early execution pattern at a handling cost to handle the early execution pattern;Vega 0084-0085: analyzing said data for energy consumption by one or more energy devices associated with said premises, (vii) computation of energy costs using said converted and stored historical energy usage data…cause the processor to partition historical data, aggregate, compare and analyze said data using at least common time period and time slice information for each premises of the plurality of premises; a second memory for separately storing the preselected data from multiple sources that comprises historical energy usage data for preselected locations for the premises… determination of unintended energy consumption, efficiency of energy consumption calculated energy optimization score (e.g., Energy IQ), comparisons of energy usage for similar reference premises at the preselected locations for preselected time periods, baseline energy consumption including breakdowns for devices, periodic comparisons of baseline to actual consumption, listing of variances between baseline and actual usage, recommendations for correcting variances, recommendations for corrections of variances to reduce energy consumption to correct variances and reduce consumption, recommendations for adjustment in preference and schedule data for a user to control and reduce energy consumption and environmental impact to correct variances and reduce consumption..0328: Display and Side by side comparison of top optimization recommendations identified based on weighted scores calculated based on the customer's optimization criteria [0329] Graphical comparison of the current condition and recommended optimization projected costs based on baseline historical and predicted consumption [0330] Comparison of environmental impact of the current and recommended condition and recommended optimization projected costs based on baseline historical and predicted consumption [0331] Interface to actuate the optimization recommendations [0332] Interface to set the actuation mode for the optimization recommendations [0333] (e.g. manual, automatic, hybrid), and to define automation settings, notifications and thresholds…Vega 0254: “Following this additional data input, the method may next generate personalized and customizable actionable information 1380 such as, for example, but not limited to visualization of time-series usage data for comparison against localized and regional locations (benchmarking), unique energy usage breakdown by appliances (interior and exterior), unique energy efficiency indicators for cost savings, energy reduction, and consequently the environmental impact savings by the consumer. This information may then be used to calculate a base line with descriptive and predictive analytics and use that as a basis for determining deviations or variations (using an end-user's criteria) 1390.”
As per claim 15, Vega and Murai teach all the limitation of claim 1.
In addition, Vega teaches:
the operation plan creation device according to claim 1; and a plan viewing server connected to the operation plan creation device via a network, wherein the plan viewing server provides the operation plan created by the operation plan creation device according to a request from the user via the network, and receives an input on the operation plan from the user;Vega 0146: FIG. 7 depicts a flow diagram of the major processing steps that the platform of FIG. 6 may employ to receive and analyze the various data from the plurality of databases…0172: Turning now to the drawings, wherein like reference numerals refer to like elements, FIG. 6 depicts a simplified block diagram of one embodiment or configuration of the platform or system 100 for end-use analytics and optimization of energy consumption of the present disclosure (hereinafter referred to as “platform” or “system”) to establish a baseline for energy consumption and monitoring actual consumption for variances from the baseline and the determination of the cause for and correction of any variance. The system 100 includes an analytics and statistical analysis component, which may include an energy analytics engine (optimization advisory engine) (optimization advisory engine)110 and one or more databases 112, 114, 116, 120, and 143, as discussed in more detail herein below. The energy analytics engine (optimization advisory engine) 110 may include a processor 1201 and a memory 1221 that can communicate via a bus or any other appropriate communication means 124. Although depicted as a single block representing a processor and a single block representing a memory in FIG. 6, a processor 1201 of the system of the present disclosure may be one or more processors and similarly for the memory 1221, a memory may be one or more memories.”
Claims 8-9 are directed to the method for performing the CRM of claims 1-2 above. Since Vega and Murai teach the method, the same art and rationale apply.
Claims 3-4 and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20210123771 (hereinafter “Vega”) et al., in view of U.S. PGPub 20110270459 to (hereinafter “Murai”) et al., in further view of U.S. PGPub 20170207629 to (hereinafter “Seki”) et al.
As per claim 3, Vega and Murai teach all the limitations of claim 2.
In addition, Vega teaches:
the cooperative operation plan optimization problem creation unit evaluates a cost due; Vega 0208: “then a calculation and comparison of average hourly electricity usage during idle and away periods may be made, followed by an estimation of the resulting associated cost and emissions 7322 to the environment. The results from these calculation and comparison steps may then be displayed 7324 as descriptive analytics in formats such as for example, but not limited to idle and away electricity consumption and associated energy leakage, cost and emissions over a period of time (e.g., annual, monthly, weekly, etc.). These steps are briefly summarized herein below regarding FIG. 21…0288: a customer is provided with time series monitoring of actual versus forecasted energy usage 13020 with trending capabilities and the quantified changes in key energy indicators (including costs, efficiency, energy leakage, and environmental footprint) during a given period via an energy dashboard that is updated using new energy consumption…0343: Machine learning algorithms continuously learn new patterns and update existing patterns from the users refining suggestions and actions to improve energy consumption with minimal discomfort (FIG. 41 depicts one representative example of a machine learning method to accomplish this action).”Vega and Murai may not explicitly teach the following. However, Seki teaches:
to execution delay of a workload and a required energy cost of a consumption pattern presented by the data center operator, and calculates a required cost correspondingly; Seki 0112: “the production executing system 22 delays the operating time period of the production facility 242-3 by 300 (minutes). That is, the production executing system 22 delays energy supply instructions to be transmitted to the energy control system 231 provided in the energy section 23 or production instructions to be transmitted to the production facility control system 241 provided in the production section 24 by 300 (minutes). By the processes of steps S110 to S120, the system (not illustrated) that manages the always responding facility stops the lighting facility 243-2 and the air-conditioning facility 244-2 after the elapse of a responding time (minutes) of 3 (minutes). In this way, it is possible to reduce electric power of 1.7 (MW) which is the sum of 0.9 (MW) from the production facility 242-3 and 0.8 (MW) from the lighting facility 243-2 and the air-conditioning facility 244-2. The demand response determination system 25 transmits information indicating that the request of the demand response is accepted and information indicating that electric power of 1.7 (MW) is reduced to the sales order system 11. In this way, the sales order system 11 transmits a DR response signal indicating the requested demand response is accepted and demand information indicating that electric power of 1.7 (MW) is reduced to the demand response transaction server 41 of the electricity provider 40 on the basis of the information transmitted from the demand response determination system 25.”
Vega, Murai, and Seki are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Vega with the aforementioned teachings from Seki and Murai with a reasonable expectation of success, by adding steps that allow the software to delay execution with the motivation to more accurately analyze and implement [Seki 0112].
As per claim 4, Vega, Murai, and Seki teach all the limitations of claim 3.
In addition, Vega teaches:
wherein when the consumption pattern of the user calculated by the operation plan draft calculation unit is an early execution pattern of a consumption pattern that responds in the past, calculation in the cost calculation unit is simplified by providing the corresponding consumption pattern and calculated job queue to the workload execution management server; Vega 0288: “a customer is provided with time series monitoring of actual versus forecasted energy usage 13020 with trending capabilities and the quantified changes in key energy indicators (including costs, efficiency, energy leakage, and environmental footprint) during a given period via an energy dashboard that is updated using new energy consumption and locational weather data retrieved and integrated at different time periods (e.g., real time/near real time, hourly, daily, weekly, monthly) from data sources (e.g., smart devices, metering systems, server, data lake, etc.). FIGS. 30-33 depict some representative examples of these types of data and analysis. Similarly, FIG. 35 displays actual monthly period to period electricity usage variations compared to average outdoor temperatures for those months as one example of descriptive analytics. FIG. 35 displays predicted (predictive analytics) electricity consumption based on personalized fitting functions, and time and weather factors. FIG. 36 compares actual consumption usage vs. projected predictions and may be used to determine variation (statistical sigmas)…0328: “Display and Side by side comparison of top optimization recommendations identified based on weighted scores calculated based on the customer's optimization criteria [0329] Graphical comparison of the current condition and recommended optimization projected costs based on baseline historical and predicted consumption [0330] Comparison of environmental impact of the current and recommended condition and recommended optimization projected costs based on baseline historical and predicted consumption [0331] Interface to actuate the optimization recommendations [0332] Interface to set the actuation mode for the optimization recommendations [0333] (e.g. manual, automatic, hybrid), and to define automation settings, notifications and thresholds.
Claims 10-11 are directed to the method for performing the CRM of claims 3-4 above. Since Vega, Murai, and Seki teach the method, the same art and rationale apply.
Claims 5-7 and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. PGPub 20210123771 (hereinafter “Vega”) et al., in view of U.S. PGPub 20110270459 to (hereinafter “Murai”) et al., in further view of U.S. PGPub 20170207629 to (hereinafter “Seki”) et al., and in further view of U.S. PGPub 20160141873 (hereinafter “Ellice”) et al.
As per claim 5, Vega, Murai, and Seki teach all the limitations of claim 4.
Vega, Murai, and Seki may not explicitly teach the following. However, Ellice teaches:
wherein the cooperative operation plan optimization problem creation unit optimizes a power interchange plan according to a request from a power retailer that includes a supply-and- demand adjustment server for planning a power interchange among the data center, another data center service operator, and another distributed energy resource operator, and during execution of the optimization, an operation plan in the data center is re-created repeatedly until an end condition for achieving adjustment of the power interchange is satisfied; Ellice 0145-0230: “The control device further comprises at least one electrically controllable switch or power flow-gate. The switch or power flow-gate performs the action of electrical connection or disconnection of at least a portion of the second electrical network 250 from the first electrical network 210. The power flow-gate can further augment the type of power available to the second electrical network 250. For example, a flow-gate may comprise a power controlling device capable of controlling the average power available to a load connected within the second electrical network 250….a fossil-fuelled power generation plant 1750 offers the potential for on-demand power generation and is therefore fully dispatchable at call by the demand scheduler of the control device 520 and can therefore advantageously engage with the real-time market if required. For such a case where all three phases 415, 416 & 417 are utilised within the second electrical network 1705, then appropriate power measurement devices and smart transfer switches can be utilised for control of three independent second electrical network loads generalised in type as element 1725.
Vega, Murai, Seki, and Ellice are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Vega, Murai, and Seki with the aforementioned teachings from Ellice with a reasonable expectation of success, by adding steps that allow the software to adjust execution with the motivation to more accurately analyze and implement [Ellice 0145].
As per claim 6, Vega, Murai, and Seki teach all the limitations of claim 2.
Vega, Murai, and Seki may not explicitly teach the following. However, Ellice teaches:
wherein the workload execution management server includes a workload control plan execution unit configured to control the execution of the workload of the user based on the operation plan; Ellice 0145-0230: “The control device further comprises at least one electrically controllable switch or power flow-gate. The switch or power flow-gate performs the action of electrical connection or disconnection of at least a portion of the second electrical network 250 from the first electrical network 210. The power flow-gate can further augment the type of power available to the second electrical network 250. For example, a flow-gate may comprise a power controlling device capable of controlling the average power available to a load connected within the second electrical network 250….a fossil-fuelled power generation plant 1750 offers the potential for on-demand power generation and is therefore fully dispatchable at call by the demand scheduler of the control device 520 and can therefore advantageously engage with the real-time market if required. For such a case where all three phases 415, 416 & 417 are utilised within the second electrical network 1705, then appropriate power measurement devices and smart transfer switches can be utilised for control of three independent second electrical network loads generalised in type as element 1725.
Vega, Murai, Seki, and Ellice are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Vega, Murai, and Seki with the aforementioned teachings from Ellice with a reasonable expectation of success, by adding steps that allow the software to adjust execution with the motivation to more accurately analyze and implement [Ellice 0145].
As per claim 7, Vega, Murai, Seki, and Ellice teach all the limitations of claim 6.
In addition, Seki teaches:
wherein in a case in which the cost calculation unit of the user is not available or a response speed thereof is not sufficient when a replan is requested from the operation plan server, the operation plan draft calculation unit creates, based on a plurality of consumption patterns registered in advance and handling costs thereof, an optimal operation plan draft by setting, as a search range, only a consumption pattern that is achievable by early executing a calculated job queue in the registered consumption patterns; Seki 0136: “the demand response determination system 25 determines whether the gain/loss of the amended production plan output from the production plan amendment implementation determination system 26 via the production plan drafting system 21 meets a predetermined gain/loss (for example, whether the gain/loss is within a predetermined threshold gain/loss range (step S232). That is, the demand response determination system 25 determines whether the amended production plan is an implementable production plan in step S232…0192-0196: The comparator 267 compares a predetermined gain/loss threshold (hereinafter referred to as a “demand response determination threshold”) for determining whether or not to respond to the demand response and the demand response gain/loss output from the risk subtractor 266 and outputs information indicating the comparison result to the plan changeability determiner 268. For example, the comparator 267 outputs a result obtained by calculating the difference between the value of the demand response gain/loss and the demand response determination threshold to the plan changeability determiner 268 as a comparison result.
Vega and Seki are deemed to be analogous references as they are reasonably pertinent to each other and directed towards measuring, collecting, and analyzing information with a series of inputs to solve similar problems in the similar environments. Before the effective filing date of the claimed invention, it would have been obvious for one of ordinary skill in the art to have modified Vega with the aforementioned teachings from Seki with a reasonable expectation of success, by adding steps that allow the software to adjust execution with the motivation to more accurately analyze and implement [Seki 0192].
Claims 12-14 are directed to the method for performing the CRM of claims 5-7 above. Since Vega, Murai, Seki, and Ellice teach the method, the same art and rationale apply.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
McNally; James T.. Energy And Cost Savings Calculation System, .U.S. PGPub 20060167591 The present invention relates generally to systems for monitoring energy use parameters within a building. More particularly, the present invention relates to systems for predicting thermal and power use conditions to be encountered by a building and by virtue of comparing the predicted conditions with actual conditions, the energy and cost savings may be determined.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Arif Ullah, whose telephone number is (571) 270-0161. The examiner can normally be reached from Monday to Friday between 9 AM and 5:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner’s supervisor, Beth Boswell, can be reached at (571) 272-6737. The fax telephone numbers for this group are either (571) 273-8300 or (703) 872-9326 (for official communications including After Final communications labeled “Box AF”)./Arif Ullah/Primary Examiner, Art Unit 3625