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 .
This office action is in response to communication filed on 1/16/2025.
Claims 1-20 are presented for examination.
Claim Objections
Claim 1 is objected to because of the following informalities: Each step of the claim should end with a semicolon to differentiate the elements from the components.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-2 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 limitation "the network load" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 1 recites the limitation "the sector" in line 6. There is insufficient antecedent basis for this limitation in the claim.
Claim 2 depends from claim 1, and calls for “a network operator interface” in line 2, but is unclear if is the same -network operator interface- of claim 1, line 3.
Claim 2 depends from claim 1, and calls for “data and information” in line 2, but is unclear if is the same -data and information- of claim 1, line 4.
Claim 4 recites the limitation "the service provider" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 depends from claim 1, and calls for “network operator” in line 3, but is unclear if is the same -network operator- of claim 1, line 2.
Claim 9 depends from claim 1, and calls for “a predictive model” in line 3, but is unclear if is the same -predictive model- of claim 1, line 2.
Claim 10 depends from claim 1, and calls for “a predictive model” in line 2, but is unclear if is the same -predictive model- of claim 1, line 2.
Claim 10 depends from claim 1, and calls for “a network operator” in lines 2-3, but is unclear if is the same -network operator- of claim 1, line 2.
Claim 12 depends from claims 1-2, and calls for “one network operator” in lines 2-3, but is unclear if is the same -network operator- of claim 1, line 2.
Claim 13 depends from claims 1-3 , and calls for “one network operator” in lines 2-3, but is unclear if is the same -network operator- of claim 1, line 2.
Regarding claim 1 the phrase "capable of ", line 6 renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Regarding claim 4 the phrase "assumes ", line 2 renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Determining that a claim falls within one of the four enumerated categories of patentable subject matter recited in 35 U.S.C. 101 (i.e., process, machine, manufacture, or composition of matter). (MPEP 2106.03)
Claims 1-16 describe a series of steps, thus falling within one of the four statutory classes; i.e., process.
Step 2A, Prong One: Evaluating whether the claim(s) recite(s) a judicial exception, i.e. whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. (MPEP 2106.04).
Representative claim 1 recites:
A method for optimizing load in at least one predefined sector of at least one operator, using a predictive model for load management, wherein operator is provided, data and information from model calculations and individual data from users of a service provider are employed, wherein individual communication is executed at least with users who are capable of supplying power in the sector.
The claims in general relate to optimizing a load in one predefined sector using a prediction model. The limitations as drafted, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting “electrical network”, “interface” nothing in the claim elements precludes the steps from practically being performed in the mind. For example, but for the “electrical network” and “interface”, the context of this claim encompasses actions that a human could perform; e.g., a human can optimize a load by making calculations which will help make a prediction.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: Identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and then evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application. Prong Two distinguishes claims that are "directed to" the recited judicial exception from claims that are not "directed to" the recited judicial exception. (MPEP 2106.04).
This judicial exception is not integrated into a practical application. In particular, the claim 1 recites the additional elements of “electrical network”, “interface” are considered as “apply it” as the claim invokes the computer as a tool to perform the abstract idea. See MPEP 2106.05(f)(2) (similar to Apple, Inc. v Ameranth and Intellectual Ventures I LLC v Capital One Bank (USA).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (MPEP 2106.05(f) Mere Instructions To Apply An Exception).
Regarding the limitations “electrical network”, “interface”, as seen above, these limitations have been interpreted as “apply it”. However, these limitations can be additionally interpreted as insignificant extra-solution activity. As such, this limitation alone and in combination, does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. (MPEP 2106.05(g) Insignificant Extra-Solution Activity).
Therefore, under Step 2A, Prong Two, the claims are directed to an abstract idea.
Step 2B: Identifying whether there are any additional elements (features/limitations/steps) recited in the claim beyond the judicial exception(s), and then evaluating those additional elements individually and in combination to determine whether they contribute an inventive concept (i.e., amount to significantly more than the judicial exception(s)). (MPEP 2106.05)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “electrical network”, “interface”, alone or in combination amount to no more than mere instructions to apply the exception using generic computer components.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept.
Regarding the limitations Regarding the limitations “electrical network”, “interface”, it is noted that sending information over a network has been recognized in the courts as being Well Understood Routine and Conventional (see MPEP 2106.05(d)(II) - i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network).
Therefore, this additional element does not amount to significantly more than a judicial exception and cannot provide an inventive concept. (MPEP 2106.05(d) Well-Understood, Routine, Conventional Activity).
Dependent claims 2-20 further narrow the abstract idea and therefore are not patent eligible.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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 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 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by
(DE 102019215609 Andreas).
With respect to claim 1, Andreas teaches a method for optimizing network load in the at least one predefined sector of an electrical network (see page 4, paragraph 4 for One optimization goal would be, for example, minimization or "fast" charging, i.e. charging at maximum speed or maximum charging power. Another goal of this kind could be to use electricity tariffs that vary over time, to use renewable energies or to charge additional electricity that can later be fed back into the grid for charging), using a predictive model for load management (prediction modules 605 , 606 , 607 and 608 on page 7, 2nd paragraph); at least one network operator (network operators 619) is provided data and information from model calculations and individual data from users of a service provider are employed (see page 7, 1st paragraph for the calculation of the charging schedule starts with the modules checking historical or current mobility and vehicle data that have already been saved 601 and 602 with external customer data), wherein individual communication is executed at least with users who are capable of supplying power in the sector (see page 8, 1st paragraph for the calculated loading plan from module 613 can through module 614 can be visualized. The visualization can include any end-user device, as well as the vehicle HMI 615 take. In a further embodiment, the loading plan can be completely or partially with other vehicles 620 , IoT devices 621 , Aggregators 618 , Energy markets 616 , Network operators 619 , intelligent home 617 and intelligent infrastructure providers 622 shared to enable iterative optimization).
With respect to claim 2, Andreas further teaches optimizing wherein a network operator interface is provided and data and information are generated from model calculations of an environment model and a fleet model for a predefined network region (see page, 7, 5th paragraph for the energy demand between a pair of locations is determined by the vehicle energy consumption module 610 derived. The prediction is based on parameters of the module 607 and external data. The external data can include, among other things, ambient temperature and average speed profiles from real-time traffic providers), wherein the fleet model includes sub-models comprised of a customer preference user model , a utilization group model , a charging column model , a vehicle model and a driver model (see page 2, 8th paragraph for a vehicle fleet is controlled in such a way that the charging power of each electric vehicle is set in cooperation with other electric vehicles and the corresponding charging points. The ability of the control can also be used to provide various stabilization services for the power grid (e.g. demand control, frequency regulation, "peak shaving", "demand response", a quick response to strongly fluctuating renewable energy sources, etc.). Similar to electric vehicles, the charging of other energy storage devices, such as private energy storage systems, can also be controlled in order to stabilize the power grid).
With respect to claim 3, Andreas further teaches wherein the sector is defined by a network load model (see page 2, 1st paragraph for load on a power grid) and a network node model (see page 2, 1st paragraph for network communication concept that enables vehicles to control the power consumption, but also allows the power grid to optimize the power consumption of one or more vehicles connected to the power grid).
With respect to claim 4, Andreas further teaches wherein the service provider assumes the provision of services to subscribed users, and the delivery of further services to the at least one network operator (see page 8, 1st paragraph Aggregators 618 , Energy markets 616 , Network operators 619 , intelligent home 617 and intelligent infrastructure providers 622 shared to enable iterative optimization. This can also include a dynamic change of incentives for other interested parties).
With respect to claim 5, Andreas further teaches wherein users both supply and consume power (see page 2, 1st paragraph for vehicles to control the power consumption, but also allows the power grid to optimize the power consumption of one or more vehicles connected to the power grid).
With respect to claim 6, Andreas further teaches wherein the network operator actively controls their network in the sector, by means of the service provider such that network capacity utilization at any time is optimized (see page 6, paragraph 1 for depending on the optimization target, the planner can, for example, use a constant charging profile of 3 kW in the first Plan a time window of over an hour in order to supply the battery with a minimum of energy. The planner can then pause the charging process for an hour and then charge continuously with a power of 1 kW until the battery is charged).
With respect to claim 7, Andreas further teaches wherein the service provider operates an incentive model which offers financial incentives or privileges to subscribed users, either on the Internet or in real space (see page 8, 1st paragraph for dynamic change of incentives for interested parties).
With respect to claim 8, Andreas further teaches wherein the environmental model contains information on current local weather conditions supplied by vehicles, and is additionally capable of representing a route of an electric vehicle in a region of the predetermined network sector, and of employing traffic density along the route as a parameter, the customer preference user model comprises at least information with respect to the temporal use of the electric vehicle and its known and most probable routes, situational responses to traffic conditions and data on charging behavior (see page, 7, 5th paragraph for the energy demand between a pair of locations is determined by the vehicle energy consumption module 610 derived. The prediction is based on parameters of the module 607 and external data. The external data can include, among other things, ambient temperature and average speed profiles from real-time traffic providers);
wherein data on charging behavior is detected automatically by way of charging profiles and/or a data capture for the user is provided, wherein a customer interface is employed for the input of preferences on charging points, distance from destination, adaptation of charging profiles to the charging time, charging energy and charging power, wherein the utilization group model is dynamically structured by the correlation of equivalent utilization groups and/or equivalent user behavior (see page 3, paragraph 1 for collect usage profiles from a user or from several user groups. Information about existing conditions such as climate or weather, current price signals for the individual players, CO2, in connection with electricity generation or environmental conditions can also be relevant for energy consumption);
wherein the charging column model delivers weather information, occupancy data, functional and capacity data, together with type information on charging columns, wherein the vehicle model is employed for determining the state-of-charge at the end of the journey, which is also based upon a prediction, wherein the driver model for ascertaining the anticipated route and individual driver behavior enables a further improvement in the estimation of anticipated energy consumption (see page 5, 4th paragraph for future energy requirements can be determined based on predictions that include a known or estimated departure time, a known or estimated distance before the next charging facility, or a known or estimated heating or cooling requirement),
wherein the fleet model aggregates available and calculated data from individual models by way of the customer preference user model, the utilization group model , the charging column model , the vehicle model and the driver model , in order to execute the delivery and transfer, at the network operator interface, of a forecast for the future anticipated location-based power and energy demand (see page 2, paragraph 8 for to reduce a charging power can be advantageous for the power grid if the current electric charging power of a vehicle fleet is controlled in such a way that the charging power of each electric vehicle is set in cooperation with other electric vehicles and the corresponding charging points. The ability of the control can also be used to provide various stabilization services for the power grid (e.g. demand control, frequency regulation, "peak shaving", "demand response", a quick response to strongly fluctuating renewable energy sources, etc.).
With respect to claim 9, Andreas further teaches a service package comprising data from calculated models which are consolidated into a predictive model which is provided to network operators for the network operation thereof, and a service provision package for subscribed users and at least one network operator (see page 2, 8th paragraph for services for the power grid (e.g. demand control, frequency regulation, "peak shaving", "demand response", a quick response to strongly fluctuating renewable energy sources, etc.). Similar to electric vehicles, the charging of other energy storage devices, such as private energy storage systems, can also be controlled in order to stabilize the power grid).
With respect to claim 10, Andreas further teaches a business model for the supply and commercial sale of calculated data from a predictive model for a network operator and subscribed users (see page 2, 5th paragraph for typical electric vehicle (EV) can use between 5 and 20 kWh of electrical energy per day to charge the traction battery. Other electrical drive means, such as e-bikes or electric scooters, generally require less, whereas commercial vehicles require considerably more. The time to transfer the amount of energy from the power grid to the vehicle battery can be 90 minutes, for example).
With respect to claim 11, Andreas further teaches wherein the sector is defined by a network load model (see page 2, 1st paragraph for load on a power grid) and a network node model (see page 2, 1st paragraph for network communication concept that enables vehicles to control the power consumption, but also allows the power grid to optimize the power consumption of one or more vehicles connected to the power grid).
With respect to claims 12-13, Andreas further teaches wherein the service provider assumes the provision of services to subscribed users, and the delivery of further services to the at least one network operator (see page 8, 1st paragraph Aggregators 618 , Energy markets 616 , Network operators 619 , intelligent home 617 and intelligent infrastructure providers 622 shared to enable iterative optimization. This can also include a dynamic change of incentives for other interested parties).
With respect to claims 14-16, Andreas further teaches wherein users both supply and consume power (see page 2, 1st paragraph for vehicles to control the power consumption, but also allows the power grid to optimize the power consumption of one or more vehicles connected to the power grid).
With respect to claims 17-20, Andreas further teaches wherein the network operator actively controls their network in the sector, by means of the service provider such that network capacity utilization at any time is optimized (see page 6, paragraph 1 for depending on the optimization target, the planner can, for example, use a constant charging profile of 3 kW in the first Plan a time window of over an hour in order to supply the battery with a minimum of energy. The planner can then pause the charging process for an hour and then charge continuously with a power of 1 kW until the battery is charged).
Refences of record but not applied to the current rejections:
Wang (12,221,006) teaches first charging data corresponding to the at least one first charging operation received from each of the electric vehicle charging stations during the first predetermined period, the server generates an energy prediction data of the charging field in a second predetermined period, wherein the energy prediction data includes at least an energy consumption estimation of the charging field at a specific time point.
Article by Ferreira, titled “Smart Electric Vehicle Charging System” teaches control of power available in the electrical network based on BW paradigms are emerging, like the Electric Vehicle historical data}, and later, the proposed system, integrated on ‘ (EV), the Smart Grids (SG), the Vehicle-to-Grid a SG, also could execute tasks related with EM Grinding the (V2G)}, and the Electrical Markets (2M}.
Conclusion
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/RAQUEL ALVAREZ/Primary Examiner, Art Unit 3622
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