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 .
Claims 1-22 are pending.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-8 and 11-22 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yang et al. USPGPUB 2021/0003974 (hereinafter “Yang”).
As to claim 1, Yang teaches a computer-implemented method, comprising: generating power forecast data for a power system using a forecasting model, wherein: the power forecast data includes predicted power consumption and generation data for the power system the forecasting model receives power consumption and generation data for the power system as input and outputs the predicted power consumption and generation data (paragraph 0006-0007 “processor executes the instructions based on a predictive machine learning model that forecasts status of at least one source of electrical power, predicts demands by the one or more associated electrical devices for the electrical power, and predicts availability of the electrical power at the times the electrical power is demanded by the one or more associated electrical devices. The predictive machine learning model may include a long short-term memory machine learning based prediction model that forecasts demand on the source(s) of electrical power by the one or more associated electrical devices from historical data. The processor may further execute the instructions to transfer learned rules from the predictive machine learning model to a newly connected electrical device”, “processor further executes the instructions to communicate status data of the source(s) of electrical power and the one or more associated electrical devices to another control module and/or a cloud server. The processor further monitors power usage by passively monitoring status of a neighboring electrical device, a household electrical circuit, a neighborhood substation, and/or a power grid. The processor also may execute the instructions to schedule electrical charge and/or discharge functions and energy storage functions of the one or more associated electrical devices based on the status of the source(s) of electrical power, the electrical usage of the one or more associated electrical devices, and/or operational rules”, and 0071 “ML-based online control system is modeled as a constrained optimization that maximizes the payout while following the regulation signal and minimizing the cost of battery degradation. The state of charge of the battery is updated based on a linear differential equation, where the next state is based on the previous state plus current charge/discharge, while constraining the rate of charge/discharge between 0 and the maximum battery power, and the state of charge between the min/max allowed state of charge. The min/max allowed state of charge is set by the user with battery performance and lifetime considered”); generating a control parameter value for equipment of the power system using an optimization model, wherein the optimization model receives the predicted power and generation data as input and outputs the control parameter value according to an optimization goal (paragraph 0141-0142 “For example, several ML devices 2330 may use machine learning techniques to find a desirable state such as the optimal schedule for a set of common household devices: a dishwasher, washer, drier, electric AC/heat unit, electric vehicle (EV) charger, refrigerator, lighting etc. Such an optimization presents a combinatorial optimization problem (COP), with the additional difficulty that the costs associated with each component are dynamic and may vary with time. The associated costs for each component at a given time (t) can be driven by an active signal, for example dynamic electricity pricing during the day, or are inferred from a predictive model, such as estimated availability of renewables. In this optimization problem, there are N components (each consuming some amount of electricity) from which a schedule based on operational rules is to be generated that will get all components to desirable states by the end of a time period T. Thus, the COP framework may be used to establish a cost function for a set goal (e.g., maximum use of renewable energy) that is computed by summing up the energy usage (cost) of running each component that is being monitored.
[0142] For example, the desirable state for electric heaters would be a comfortable temperature range of 19-22° C. A desirable state for electric vehicle (EV) chargers could be having its batteries fully charged. A desirable state for a washer/driver could be having the laundries finished. There is an additional constraint that all devices must reach their desirable state within a given time-period, for example 24 hours. Also, due to the current limit in a household, only a small set of electronics can be operated at the same time, before the breaker kicks in”); and transmitting the control parameter value to the equipment to cause the equipment to operate according to the control parameter value (paragraph 0141-0142 “managed to optimize a local network (household) of different types of devices. For example, several ML devices 2330 may use machine learning techniques to find a desirable state such as the optimal schedule for a set of common household devices: a dishwasher, washer, drier, electric AC/heat unit, electric vehicle (EV) charger, refrigerator, lighting etc. Such an optimization presents a combinatorial optimization problem (COP), with the additional difficulty that the costs associated with each component are dynamic and may vary with time” and 0165-0180).
As to claim 2, Yang teaches wherein the optimization model is one of a plurality of optimization models, each optimization model of the plurality of optimization models including a respective optimization goal for generating control parameter values for one or more pieces of power system equipment (paragraph 0150-0159 “Using such an optimization approach in a sample embodiment, an example solution for the optimization of renewable energy usage could learn the following operations: [0151] 1. Stop cooling the house at 9 am, when no one is home. [0152] 2. Start the washer/drier from 9 am-12 pm. [0153] 3. Charge the EV from 12 pm-2 pm. [0154] 4. Start to cool down the house from 2 pm-4:30 pm to 17° C. (a temperature that is lower than desired range of 19°-22° C.). [0155] 5. Turn off the air conditioning and let the temperature drift slowly upward as people return home. [0156] 6. Run the dishwasher from the electricity stored in the EV's batteries at 8 pm. [0157] 7. Run the air conditioning on the EV batteries at 9:30 pm but keep the house at 22° C. [0158] 8. Start charging the EV batteries slowly at 4 am, knowing that the EV needs to be fully charged at 8 am. As the day breaks, draw more power from the solar generators……………).
As to claim 3, Yang teaches wherein generating the control parameter value includes generating a dispatch schedule including a time period during which the equipment is to be operated according to the control parameter value (paragraph 0150-0159).
As to claim 4, Yang teaches wherein transmitting the control parameter value to the equipment is by transmitting the dispatch schedule to the equipment, wherein when the equipment receives the dispatch schedule, the equipment executes the dispatch schedule to operate according to the control parameter value during the time period (paragraph 0150-0159).
As to claim 5, Yang teaches wherein the forecasting model is one of a plurality of forecasting models, each forecasting model of the plurality of forecasting models configured to predict at least a portion of power consumption and generation for the power system (paragraph 0006-0007, 0071, 0141-0142 and 0150-0159).
As to claim 6, Yang teaches further comprising: receiving a model parameter value for a model, wherein the model is one of the forecasting model and the optimization model; and responsive to receiving the model parameter value, updating the model according to the model parameter value (paragraph 0008 and 0131).
As to claim 7, Yang teaches further comprising: transmitting operational data for the power system to a centralized computing system, wherein the centralized computing system is configured to receive power system operational data and to revise models for managing power systems according to the power system operational data (paragraph 0129-0130, 0160 and 0185); receiving a model parameter value from the centralized computing system for a model managed by the centralized computing system, wherein the model managed by the centralized computing system is one of the forecasting model and the optimization model; and responsive to receiving the model parameter value, updating the model according to the model parameter value (paragraph 0129-0134, 0185 and 0160).
As to claim 8, Yang teaches further comprising: obtaining the power consumption and generation data for the power system (paragraph 0128-0129); and providing the power consumption and generation data to the forecasting model, wherein obtaining the power consumption and generation data for the power system includes collecting power consumption and generation data from the equipment of the power system (paragraph 0128-0130).
As to claim 11, Yang teaches wherein at least one of the forecasting model and the optimization model further receives, as input, power grid data for a power grid electrically coupled to the power system, wherein the power grid data includes at least one of: cost data for power provided by the power grid; emissions data for power provided by the power grid; and demand data for power provided by the power grid (paragraph 0128-0129).
As to claim 12, Yang teaches wherein at least one of the forecasting model and the optimization model further receives, as input, environmental data for a geographic region around the power system, wherein the environmental data includes at least one of: temperature data for the geographic region; wind data for the geographic region; cloud cover data for the geographic region; precipitation data for the geographic region; sunlight data for the geographic region; and daylight data for the geographic region (paragraph 0127-0128).
As to claim 13, Yang teaches wherein the optimization goal includes at least one of energy consumption reduction by the power system, emissions reduction, or cost reduction (paragraph 0139).
As to claim 14, Yang teaches wherein the equipment is at least one of a load that consumes power, an energy source that generates power, or an energy storage device that stores power (paragraph 0150-0159).
As to claim 15, Yang teaches wherein the equipment is an inverter (paragraph 0167).
As to claim 16, Yang teaches a non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to execute a method of claim 1 (paragraph 0188 and same as claim 1).
As to claim 17, Yang teaches a computer-implemented method, comprising: receiving operational data for a power system managed by an energy management system (EMS), wherein: an equipment of the power system includes at least one of power generation equipment, power storage equipment, power conversion equipment, and a load (paragraph 0167 and 0150-0159); and the EMS includes a forecasting model configured to receive power consumption and generation data for the power system as input and to output predicted power consumption and generation data (paragraph 0006-0007 and 0071); and the EMS further includes an optimization model configured to receive the predicted power consumption and generation data as input and to output a control parameter value for equipment of the power system according to an optimization goal (paragraph 0141-0142); determining an updated model parameter for a master model based on the operational data including power generation data or power consumption data for equipment of the power system wherein the master model corresponds to one of the forecasting model and the optimization model (paragraph 0008, 0131-0134); and transmitting the updated model parameter to the EMS, wherein, when the updated model parameter is received by the EMS, the EMS updates the one of the forecasting model and the optimization model according to the updated model parameter (paragraph 0134 and 0141-0142).
As to claim 18, Yang teaches wherein: the master model corresponds to the optimization model and the forecasting model, and the optimization model is one of a plurality of optimization models of the EMS, each optimization model of the plurality of optimization models including a respective optimization goal for generating control parameter values for the equipment of the power system, the forecasting model is one of a plurality of forecasting models of the EMS, each forecasting model of the plurality of forecasting models configured to predict at least a portion of power consumption and generation for the power system, and the master model is one of a plurality of master models, each of the plurality of master models corresponding to a respective one of the plurality of optimization and forecasting models (paragraph 0141-0142 and 0150-0159).
As to claim 19, Yang teaches further comprising receiving power grid data for a power grid electrically coupled to the power system, wherein determining the updated model parameter is further based on the power grid data including at least one of: cost data for power provided by the power grid; emissions data for power provided by the power grid; and demand data for power provided by the power grid (paragraph 0129-0130).
As to claim 20, Yang teaches further comprising receiving environmental data for a geographic region around the power system wherein: the environmental data including at least one of: temperature data for the geographic region; wind data for the geographic region; cloud cover data for the geographic region; precipitation data for the geographic region; sunlight data for the geographic region; and daylight data for the geographic region; and he updated model parameter is further determined and or based on the power grid data (paragraph 012-0129).
As to claim 21, Yang teaches wherein the optimization goal includes at least one of energy consumption reduction by the power system, emissions reduction, and cost reduction (paragraph 0139).
As to claim 22, A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to perform a method of claim 17 (paragraph 0188 and as of claim 17).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 9 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yang et al. USPGPUB 2021/0003974 (hereinafter “Yang”) in view of Kagan et al. USPGPUB 2013/0204450 (hereinafter “Kagan”).
As to claim 9, Yang teaches all the limitations of the base claims as outlined above.
Yang teaches wherein the forecasting model and the optimization model (paragraph 0129-0134, 0185 and 0160 sequentially calls the forecasting model and the optimization model to generate the control parameter value (paragraph 0150-0159 and 0165) a ML controller 2740 that passively monitor the status of electricity usage of an associated energy consuming device 2750, such as a dishwasher, that is connected to that electrical outlet 2750 via the ML controller 2740. For example, the ML controller 2740 may count energy cycles and voltage drops that may occur when other devices on the same circuit turn on and start drawing more electricity. The electricity passes through the ML controller 2740 to the energy consuming device 2750, and control information, such as energy usage in kWatts/hour and timing of activation are provided as control signal back to the ML controller 2740 for use in monitoring and reporting the energy usage of the energy consuming device 2750); and transmitting the control parameter value to the equipment (paragraph 0150-0159). Yang does not teach are maintained in a logic layer accessible by an application layer, wherein the application layer sequentially calls the forecasting model and the optimization model to generate the control parameter value; and wherein the application layer interfaces with the equipment and transmitting the control parameter value to the equipment is by the application layer. However, Kagan teaches maintained in a logic layer accessible by an application layer (paragraph 0144 and 0136-0137), wherein the application layer sequentially calls the forecasting model and the optimization model to generate the control parameter value; and wherein the application layer interfaces with the equipment and transmitting the control parameter value to the equipment is by the application layer (paragraph 0144-0156). It would have been obvious to an ordinary skill in the art to modify the method as disclosed above by Yang to include Modbus server application, as disclosed by Kagan, because it allows to scan or search the database for the current readings (Kagan, paragraph 0156).
As to claim 10, the combination of Yang and Kagan teaches all the limitations of the base claims as outlined above.
Kagan further teaches wherein the application layer communicates with the equipment of the power system using one or more of Modbus and CANbus (paragraph 0144-0156 and 0136-0137).
It is noted that any citations to specific, pages, columns, lines, or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
The prior art made of record and listed on the attached PTO Form 892 but not relied upon is considered pertinent to applicant's disclosure.
Poltorak USP 11,588,650 B2 a systems and methods for supporting encrypted communications with a medical device, such as an implantable device, through a relay device to a remote server, and may employ cloud computing technologies. An implantable medical device is generally constrained to employ a low power transceiver, which supports short distance digital communications. A relay device, such as a smartphone or WiFi access point, acts as a conduit for the communications to the internet or other network, which need not be private or secure. The medical device supports encrypted secure communications, such as a virtual private network technology. The medical device negotiates a secure channel through a smartphone or router, for example, which provides application support for the communication, but may be isolated from the content.
Elbsat USP 11271769 B2 teaches a controller for building equipment that operate to provide heating or cooling for a building or campus. The controller includes a processing circuit configured to perform an optimization of an objective function subject to an override constraint to determine amounts of one or more resources to be produced by the building equipment and control the building equipment to produce the amounts of the one or more resources determined by performing the optimization subject to the override constraint. The override constraint overrides an output of the optimization by specifying an override amount of a first resource of the one or more resources to be produced by a first subset of the building equipment.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZIAUL KARIM whose telephone number is (571)270-3279. The examiner can normally be reached on Monday-Thursday 8:00-4:00 PM EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Mohammad Ali can be reached on 571 272 4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ZIAUL KARIM/Primary Examiner, Art Unit 2119
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