DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-8 are pending.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55 for Application No. EP23183176.9 filed on 07/03/2023.
Information Disclosure Statement
The references cited in the information disclosure statements (IDS) submitted on 07/03/2023 have been considered by the examiner.
Claim Objections
The following claims are objected to for informalities, lack of antecedent support, or for redundancies. The Examiner recommends the following changes:
Claim 2, line 1, replace “determining at least” with “the determining the at least”
Claim 3, line 2, replace “at least” with “the at least”
Claim 8, line 2, replace “heated” with “heater”
Appropriate correction is respectfully requested.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-8 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitations “receiving total energy load data of the system, wherein the total energy load data comprises individual energy load data of a plurality of assets” in lines 3-4, and “disaggregating the total energy load data into the individual energy load data of the plurality of assets” in lines 6-7. It is unclear what Applicant means by “disaggregating the total energy load data into the individual energy load data of the plurality of assets,” when “the total energy load data comprises individual energy load data of a plurality of assets.” (emphasis added) In other words, it is unclear how the individual energy load data of the plurality of assets get disaggregated to the individual energy load data of the plurality of assets. Appropriate clarification through claim amendment is respectfully requested. For purposes of examination, the limitations will be interpreted as “receiving total energy load data of the system”, and “disaggregating the total energy load data into the individual energy load data of the plurality of assets”, respectively.
Claims 2-8 are dependent claims of claim 1. The claim 1 is rejected under 35 U.S.C. 112(b), and therefore, claims 2-8 are rejected under 35 U.S.C. 112(b)
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
(Step 2A, Prong One)
Independent claim 1 recites, “determining at least one energy load peak in the total energy load data; disaggregating the total energy load data into the individual energy load data of the plurality of assets”.
Under its broadest reasonable interpretation, if a claim limitation covers performance that can be executed in the human mind, but for the recitation of generic electronic devices or generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Under their broadest reasonable interpretation and based on the description provided in the published specification, such as paragraphs [0018] and [0035], for instance, each of the determining and disaggregating function is a mental process that can be performed through observation, evaluation and judgement based on a acquired load data of the plurality of assets. That is, a person may perform, through observation, evaluation and judgement, the features enunciated above.
Accordingly, the claim recites an abstract idea.
(Step 2A, Prong Two)
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional limitations of, “receiving total energy load data of the system, wherein the total energy load data comprises individual energy load data of a plurality of assets” and “providing the disaggregated individual energy load data of the plurality of assets for further processing”.
The additional limitation of “receiving total energy load data of the system, wherein the total energy load data comprises individual energy load data of a plurality of assets” is an insignificant extra-solution activity under MPEP 2106.05(g), without imposing meaningful limits. The limitation amounts to necessary data gathering. (i.e., all uses of the recited judicial exception require such data gathering or data output).
The claim recites the additional limitation of “providing the disaggregated individual energy load data of the plurality of assets for further processing.” The practical application requires an additional element or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. When so evaluated, the additional limitation of “providing the disaggregated individual energy load data of the plurality of assets for further processing” is merely adding the words providing … for further processing with the judicial exception that attempts to cover a solution with no restriction on what the result is and how the result is accomplished, and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it", and does not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, and therefore is not indicative of integration into a practical application, see MPEP 2106.05(f). The claim does not recite an improvement in a technology as set forth in MPEP 2106.04(d) and MPEP 2106.05(a). Accordingly, the additional limitations recited in the claim do not integrate the abstract idea into a practical application.
In view of the foregoing, the additional limitations are not sufficient to demonstrate integration of a judicial exception into a practical application.
(Step 2B)
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The receiving function represents a function that is recognized as well-understood, routine, and conventional, for instance, as demonstrated in Serven et al. (US 2020/0349484 A1) (a reference cited in the information disclosure statement submitted on 07/03/2024) paragraph [0026] (“Data collector system 120 can be coupled to the local power system 101. The data collector system 120 can be attached to local power system 101 to monitor aggregate power, or power used by an individual circuit, at a location (e.g., commercial, industrial, or residential building). In an example implementation, circuit based sensors can collect power usage data at a central location, for example, a distribution board (e.g., panelboard, breaker panel, electric panel, etc.) For example, circuit based sensors can be used at an electric panel, where a single sensor is clamped onto each circuit, and the sensors are daisy-chained together, with a data transmitter to connect to a cloud analyzer system. Circuit based sensors can be used for super-high-frequency disaggregation (e.g., 8 kilohertz).”), YONEZAWA et al. (US 2012/0065792 A1) paragraph [0071] (“Further, a distribution board 25 is arranged to receive, from the supply-demand controller 110, power generated by the power system 100 or power discharged from the power storage 130 through the power line PL. The distribution board 25 distributes the received power to the appliances 22n(1), 22n(2), and 23n and the communication controller 21n consuming power in the customer's building. The distribution board 25 measures power used by each appliance, and notifies the communication controller 21n about the measured value (power consumption) of each or all of the appliances.”), and ROMBOUTS (US 2017/0018923 A1) (a reference cited in the information disclosure statement submitted on 07/03/2024) paragraph [0101] (“Each household appliance may be connected 882-886 to computing unit 160 over a wireless low-frequency range network, e.g., in the 868 MHz band (such as the SigFox network), allowing an exchange of short messages and instructions. The power meter 872 (which may be a temporary meter) also has a connection 888 to computing unit 160 so that the main power signal is measured. The network logs the “on/off” signals of the appliances and also receives sensor data from the appliances, thus the same type of data is available as at an industrial site, and hence the above-described invention can work in the same way as at an industrial site. The data logged at site 850 may include temperature (through internal thermostat readings and external temperature data), voltage/current (from power meter), state of charge (boiler/battery), etc. Global models may be built in the same way as before. Site 850 may also include a computer 320 (or other suitable hardware) in order to communicate with each appliance and with computing unit 160 over an Internet connection, such as shown in the example of FIG. 2.”).
The additional limitation of “providing the disaggregated individual energy load data of the plurality of assets for further processing” is merely adding the word providing (or “to apply”) with the judicial exception, and does not impose a meaningful limit on practicing the abstract idea, see MPEP 2106.05(f). Thus, when taken alone, the individual additional limitations do not amount to significantly more than the above-identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually.
Therefore, the additional claimed features do not amount to significantly more and the claim is not patent eligible.
Claims 2-4 are directed to further defining the abstract idea as recited in independent claim 1. Therefore, claims 2-4 are not patent eligible.
Claim 5 and 7 are directed to further defining the extra-solution activity as recited in independent claim 1. Therefore, claims 5 and 7 are not patent eligible.
Claim 6 limitation of “providing a track record of the plurality of assets, wherein the track record is based on the disaggregated individual energy load data of the plurality of assets” is merely adding the word providing (or “to apply”) with the judicial exception, that does not integrate the abstract idea into a practical application, and does not impose a meaningful limit on practicing the abstract idea, see MPEP 2106.05(f). Therefore, claim 6 is not patent eligible.
Claim 8 limitation of “wherein the plurality of assets comprises one or more of a heated, an electric vehicle (EV) charger, a battery, a heating, ventilation, and air conditioner (HVAC) unit” as recited in the claim that are configured to carry out the additional and abstract idea limitations may be tools that are used to identify and group as recited in the claim, but recited so generically that they represent no more than mere instructions “to apply” the judicial exceptions on or using generic electronic or computer components. Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea is not indicative of integration into a practical application. see MPEP 2106.05(f). Implementing an abstract idea on generic electronic or computer components as tools to perform an abstract idea does not amount to significantly more. Therefore, claim 8 is not patent eligible.
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.
Claims 1-4, 6 and 7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Serven et al. (US 2020/0349484 A1) (“Serven”). Serven is a reference cited in the information disclosure statement submitted on 07/03/2024.
Regarding independent claim 1, Serven teaches:
A computer-implemented method for determining an energy load distribution in a system, comprising: (Serven: Abstract “A method of managing energy by use of processing logic that comprises a load processor as a cloud service is provided. The method includes receiving power load information from a data collection system located at a building and using a cloud analysis layer that employs machine-learning and artificial intelligence for optimization control, analyzing the received power load information to disaggregate load waveform signals and identify device-based power loads by use of a neural network to perform historical device demand and performance analysis to generate device-based demand forecasting, generating demand forecasts for the building to mitigate peak demand based on analysis of a power draw signal and the generated device-based demand forecasting, and determining whether the generated demand forecast for the building is to peak in a near future, based on threshold values of at least one of generated device-based demand forecasting, power price or cost information, and user behavior analysis.”) (Serven: [0025] “FIG. 1 illustrates an example diagram of a building management system 100, in accordance with an example implementation. The system 100 includes a local power system 101 that delivers power to devices 105-119 at a location. For example, the local power system 101 as part of a residential or commercial building can be a pre-existing power distribution network with devices connected via sockets (e.g., heating, ventilation and air conditioning (HVAC) 105, laptops 107, tablets 109, televisions 111, refrigerators 113, appliances 115, consumer devices 117, lights 119, garage door openers, sprinkler systems, etc.) and hard wired devices (e.g., HVAC 105, security systems, appliances 115, lights 119, sprinkler systems, etc.).”)
receiving total energy load data of the system, wherein the total energy load data comprises individual energy load data of a plurality of assets; (Serven: [0026] “Data collector system 120 can be coupled to the local power system 101. The data collector system 120 can be attached to local power system 101 to monitor aggregate power, or power used by an individual circuit, at a location (e.g., commercial, industrial, or residential building). In an example implementation, circuit based sensors can collect power usage data at a central location, for example, a distribution board (e.g., panelboard, breaker panel, electric panel, etc.) For example, circuit based sensors can be used at an electric panel, where a single sensor is clamped onto each circuit, and the sensors are daisy-chained together, with a data transmitter to connect to a cloud analyzer system. Circuit based sensors can be used for super-high-frequency disaggregation (e.g., 8 kilohertz).”) [Monitoring and collecting aggregate power usage data reads on “receiving total energy load data”. The power used by the individual circuit reads on “individual energy load data”.]
determining at least one energy load peak in the total energy load data; (Serven: [0059] “The artificial intelligence unit 360 can include a real-time performance monitor 362, an abnormality module 364, a forecast module 366, and a response module 368. The AI unit 360 includes a training process to learn patterns of the location's power performance, building behaviors or device behaviors and generates recommendation models to determine response strategies for peak demand events, abnormality events, performance improvement reports.”) (Serven: [0060] “In an example implementation, a computer readable medium includes instructions that will cause a processor that executes the instructions to receive a training data set that includes a tracking of building, circuit, or device power draw performance via the I/O interface for machine learning. The load processor 350 analyzes the training data set using machine learning to train machine-learning-based detection profiles that can be used to classify new devices and/or recognize patterns as peak or abnormal event data. The AI unit 360 uses machine-learning-based detection of patterns with labels and/or categorization to indicate device performance patterns of individual circuits and/or building level performance patterns. AI unit 360 can analyze the data to identify triggers that indicate a transition from a first pattern to a second pattern. Response strategies can be automatically implemented via the control interface 357 or reported with alerts via the reports module 359.”) [Recognizing patterns as peak at the building level reads on “determining at least one energy load peak …”.]
disaggregating the total energy load data into the individual energy load data of the plurality of assets; providing the disaggregated individual energy load data of the plurality of assets for further processing. (Serven: [0096] “In some example implementations, when information or an execution instruction is received by API unit 860, it may be communicated to one or more other units (e.g., power load analyzer unit 875, device demand forecaster unit 880 and building demand forecaster unit 885). For example, the power load analyzer unit 875 may receive power load information from a data collection system located at a building and disaggregate load waveform signals and identify device-based power loads using a neural network. The disaggregated load waveform signals and identified device-based power loads may be provided to the device demand forecaster unit 880 to perform historical device demand and performance analysis to generate device-based demand forecasting. The generated device-based demand forecasting may be provided to the building demand forecaster unit 885 to generate demand forecasts for the building to mitigate peak demand based on analysis of a power draw signal and the generated device-based demand forecasting.”) [Providing the generated device-based demand forecasting to the generate demand forecasts for the building reads on “providing the disaggregated individual energy load data … for further processing”.]
Regarding claim 2, Serven teaches all the claimed features of claim 1. Serven further teaches:
wherein determining at least one energy load peak is based on a pattern recognition algorithm. (Serven: [0060] “In an example implementation, a computer readable medium includes instructions that will cause a processor that executes the instructions to receive a training data set that includes a tracking of building, circuit, or device power draw performance via the I/O interface for machine learning. The load processor 350 analyzes the training data set using machine learning to train machine-learning-based detection profiles that can be used to classify new devices and/or recognize patterns as peak or abnormal event data. The AI unit 360 uses machine-learning-based detection of patterns with labels and/or categorization to indicate device performance patterns of individual circuits and/or building level performance patterns. AI unit 360 can analyze the data to identify triggers that indicate a transition from a first pattern to a second pattern. Response strategies can be automatically implemented via the control interface 357 or reported with alerts via the reports module 359.”)
Regarding claim 3, Serven teaches all the claimed features of claim 1. Serven further teaches:
wherein the determining at least one energy load peak is based on a trained machine learning algorithm. (Serven: [0060] “In an example implementation, a computer readable medium includes instructions that will cause a processor that executes the instructions to receive a training data set that includes a tracking of building, circuit, or device power draw performance via the I/O interface for machine learning. The load processor 350 analyzes the training data set using machine learning to train machine-learning-based detection profiles that can be used to classify new devices and/or recognize patterns as peak or abnormal event data. The AI unit 360 uses machine-learning-based detection of patterns with labels and/or categorization to indicate device performance patterns of individual circuits and/or building level performance patterns. AI unit 360 can analyze the data to identify triggers that indicate a transition from a first pattern to a second pattern. Response strategies can be automatically implemented via the control interface 357 or reported with alerts via the reports module 359.”)
Regarding claim 4, Serven teaches all the claimed features of claim 1. Serven further teaches:
wherein the disaggregating the total energy load data comprises an estimation algorithm. (Serven: [0028] “The load processor 150 maps out energy use inside of a building on a system-by-system, appliance-by-appliance level, using a minimal number of sensors located at central collection points. Circuit based sensors perform high-frequency data-energy disaggregation to deliver accurate and granular results. The sensors can catch tiny blips and squiggles in voltage and current across a building's key circuits in excruciating detail, and use that data to identify individual devices on the circuits, and then track them over time. According to some example implementations, sensors attached to a local power system can sample circuit level power usage information and detect characteristics of the building.”) [Performing disaggregation based on tiny blips and squiggles in voltage and current to identify individual devices reads on “an estimation algorithm”.]
Regarding claim 5, Serven teaches all the claimed features of claim 1. Serven further teaches:
wherein the disaggregating the total energy load data comprises receiving one or more individual energy loads of the plurality of assets by means of a measuring device. (Serven: [0028] “The load processor 150 maps out energy use inside of a building on a system-by-system, appliance-by-appliance level, using a minimal number of sensors located at central collection points. Circuit based sensors perform high-frequency data-energy disaggregation to deliver accurate and granular results. The sensors can catch tiny blips and squiggles in voltage and current across a building's key circuits in excruciating detail, and use that data to identify individual devices on the circuits, and then track them over time. According to some example implementations, sensors attached to a local power system can sample circuit level power usage information and detect characteristics of the building.”) (Serven: [0029] “In other example implementations, device performance can be remotely monitored and controlled based on machine-learning from the broad network of sensors and information. Example aspects include an automated system to map out devices that connect to a local power system to determine a device profile or fingerprint. According to such example implementations, sensors attached to a local power system can sample power usage information and detect characteristics from each device that connects to the local power system. For example, a frequency of the power draw signal can be used to categorize each device type.”)
Regarding claim 6, Serven teaches all the claimed features of claim 1. Serven further teaches:
providing a track record of the plurality of assets, wherein the track record is based on the disaggregated individual energy load data of the plurality of assets. (Serven: [0023] “Methods and systems described herein analyze an entire building to predict when the building will cross a demand threshold. Instead of just turning off an AC unit for hours, the AI unit analyzes the individual circuits or devices contributing to the peak, and identifies the least intrusive response strategy to shift demand. The AI unit determines peak demand response strategies that minimize turn-off or shutdown devices. Peak demand response strategies by the AI unit dynamically cycle different pieces of equipment (with different peak power draws) to achieve sustained reductions in total demand as needed. As the AI unit analyzes more BMS integration points, the AI unit can add response actions (e.g., pre-cooling) to a library of countermeasures.”) (Serven: [0028] “The load processor 150 maps out energy use inside of a building on a system-by-system, appliance-by-appliance level, using a minimal number of sensors located at central collection points. Circuit based sensors perform high-frequency data-energy disaggregation to deliver accurate and granular results. The sensors can catch tiny blips and squiggles in voltage and current across a building's key circuits in excruciating detail, and use that data to identify individual devices on the circuits, and then track them over time. According to some example implementations, sensors attached to a local power system can sample circuit level power usage information and detect characteristics of the building.”) (Serven: [0054] “For example, a hot-water heater or boiler may be forecasted to reach an increased power draw level during early morning hours based on occupancy information from external enrichment data, and the load processor can alert the user to abnormal power draw from the hot-water heater that is counter to historical performance in view of the occupancy. In the example, the building manager is alerted to the abnormal performance prior to a critical event and independent of a peak demand.”)
Regarding claim 8, Serven teaches all the claimed features of claim 1. Serven further teaches:
wherein the plurality of assets comprises one or more of a heated, an electric vehicle (EV) charger, a battery, a heating, ventilation, and air conditioner (HVAC) unit. (Serven: [0041] “As the signals come in from each of the circuits, the information is iteratively screened to separate the signal into multiple signals and label each signal with an identifier. The screening process analyzes a circuit signal to disaggregate the signal and identify the underlying devices receiving power via the circuit. That is, a circuit may be dedicated to a particular space (e.g., room, wall of a room, etc.) or type of device (e.g., HVAC, elevator, lights, switch, etc.) and the signal of that circuit can be analyzed to separate out signals from each device receiving power via the circuit.”) (Serven: [0054] “For example, a hot-water heater or boiler may be forecasted to reach an increased power draw level during early morning hours based on occupancy information from external enrichment data, and the load processor can alert the user to abnormal power draw from the hot-water heater that is counter to historical performance in view of the occupancy. In the example, the building manager is alerted to the abnormal performance prior to a critical event and independent of a peak demand.”)
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 7 is rejected under 35 U.S.C. 103 as being unpatentable over Serven, in view of YONEZAWA et al. (US 2012/0065792 A1) (“Yonezawa”).
Regarding claim 7, Serven teaches all the claimed features of claim 1. Serven does not expressly teach the recitations of claim 7.
Yonezawa teaches:
tagging the plurality of assets into controllable assets and non-controllable assets. (Yonezawa: [0066] “The communication controllers 21a to 21n communicate with the customer appliances 22a to 22n and the controllable appliances 23a to 23n to control these appliances. Further, the communication controllers 21a to 21n read and transmit detailed information of the customer appliances 22a to 22n and the controllable appliances 23a to 23n depending on the request from the supply-demand balance controller. The detailed information of the controllable appliances 23a to 23n includes controllable demand information (see FIG. 10 to FIG. 14) to be explained later.”) (Yonezawa: [0070] “An air conditioner 23n(1) and an electric car 23n(2) are arranged as controllable appliances. A solar power generation panel 24n is arranged as a power generator. In this case, the customer appliance 22n in not limited to a specific appliance. Note that the customer appliance 22n is a normal appliance which cannot be controlled from the outside (namely, a controllable demand controller 120).”) (Yonezawa: [0184] “The controllable demand controller 120 stores therein the control data received from the plan creator 45. The controllable demand controller 120 controls the controllable appliance of each customer based on the control data.”) (Yonezawa: [0185] “FIG. 20 shows an example of the electric supply plan created for each customer. This electric supply plan shows a plan for 10:50 to 14:00. The customer N has four controllable appliances (air conditioner A, lighting (room A), EV, and lighting). The temperature of the air conditioner A is increased by +2.degree. C. until 12:00 (power consumption is restrained), and this control is canceled after 12:00. Further, the lighting (room A) is turned off until 14:00 (demand is stopped). Power supply for charging the EV is stopped until 12:00, and this stop control is canceled after 12:00. The demand of the lighting is not particularly controlled. "Power consumption" in the drawing is the demand value predicted by this device.”) [See the labeling of the controllable appliance column, as illustrated in FIG. 20.]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Serven and Yonezawa before them, to modify the optimization control of devices in a building, to incorporate distinguishing identification between devices that are controllable and that are not controllable.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification because it would allow for controlling the controllable devices by knowing what devices are controllable to balance the supply and demand of power. (Yonezawa: [0002] “Embodiments of the present invention relate to a supply-demand balance controller in a smart grid, for example.”)
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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL W CHOI whose telephone number is (571)270-5069. The examiner can normally be reached Monday-Friday 8am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL W CHOI/Primary Examiner, Art Unit 2116
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