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 .
Status of Application
The following is a Final Office Action. In response to Examiner's communication on 09/18/2025, Applicant on 12/17/2025, amended Claims 1, 2, 5, 6, 8-18 and cancelled Claims 19-20. Claims 1-18 are now pending in this application and have been rejected below.
Response to Amendment
Applicant’s amendments are sufficient to overcome the 35 USC 112(b) rejections set forth in the previous action. Accordingly, the rejections have been withdrawn.
Applicants’ amendments are insufficient to overcome the 35 USC 101 rejections set forth in the previous action. Therefore, these rejections have been updated to address the amendments and are maintained below.
Applicants’ amendments render moot the 35 USC 103 rejections set forth in the previous action. Therefore, these rejections have been updated with new grounds necessitated by amendments and maintained below.
Response to Arguments – 35 USC § 101
Applicant's arguments with respect to the 35 USC 101 rejections have been fully considered but they are not persuasive.
Applicant argues that even if the claims recite an abstract idea, the claims are subject matter eligible by virtue of reciting an improvement to technology over conventional techniques. However, note that per MPEP 2106.05(a), "an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology." Thus, the question of whether an improvement to technology is recited turns on the context of the computing components as claimed.
To this effect, Applicant argues that the claims are part of a solution that specifies physical outcomes and physically commands machinery. Examiner notes that claims are constructed according to their broadest reasonable interpretation. In light of Page 5 of the specification, Lines 28-31, “Although in the drawings and the description below, it is a robotic charger that automatically moves to, plugs, and unplugs the PEVs, one skilled in the art can appreciate other feasible ways to implement similar functions, including but not limited to, a mobile device maneuvered by a human operator to perform the interchange operations based on optimized schedules”. As the limitations pertaining to the second number of mobile devices, as stated in amended Claim 1, solely discloses, “by the second number of mobile devices, moving among electric vehicles in the service queue to perform plugging and/or unplugging, thereby charging the electric vehicles in the service queue”, the broadest reasonable interpretation of the Claims encompasses instructing a human operator to perform the recited limitations. Therefore, Examiner must respectfully disagree with Applicant’s characterization of the claims as necessarily directed to a physical process in light of said interpretation. Because of this, the improvements and benefits of Applicant’s claimed invention are reasonably construed to apply to a process of mental planning.
For the sake of advancing prosecution, Examiner agrees that if the mobile devices were specified as autonomous robots and the method was explicitly effected by means of said robots without a human operator, it would be reasonable to consider the recited mental processes of planning as integrated into a practical application.
Response to Arguments –35 USC § 103
Applicant' s arguments with respect to the rejections under 35 USC 103 have been considered but have not been found to be persuasive.
Examiner notes that one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of
references. The test for obviousness is not that the claimed invention must
be expressly suggested in any one or all of the references. Rather, the test
is what the combined teachings of the references would have suggested to
those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ
871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed.
Cir. 1986). As stated in the preceding Non-Final Rejection, we apply the optimization method of Lee to the particular hardware configuration of Starns as outlined below. It cannot be said that the deficiencies of an individual one of the references constitute a failure of the combination to disclose.
Accordingly, the claims have been updated to address the amendments and the rejection has been maintained below.
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 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
The claims are directed to a method and apparatus. Therefore, the claims are directed to at least one of the four statutory categories.
101 Analysis – Step 2A
Regarding Prong 1 of the Step 2A analysis in the MPEP, the claims are to be analyzed to determine whether they recite subject matter that is directed to a judicial expectation, namely a law of nature, a natural phenomenon, or one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent Claim 1 includes limitations that recite an abstract idea and will henceforth be used as a representative claim for the 101 rejection until otherwise noted. Claim 1 recites:
A method for operating an electric vehicle charging station, the charging station comprising a first number of fixed chargers, a second number of mobile devices, and an operation management apparatus, each of the mobile devices being configured to move in the charging station to plug and unplug an electric vehicle, the method comprising: at a current time step within an optimization horizon including the current time step and a plurality of subsequent time steps, by the operation management apparatus, obtaining, upon receiving a charging request from an electric vehicle arriving at a beginning of the current time step, a first charging demand; by the operation management apparatus, deriving, upon receiving charging dynamics of an electric vehicle having been staying at the charging station before the current time step, a second charging demand; by the operation management apparatus, generating, with respect to the optimization horizon a charging demand forecast; by the operation management apparatus, solving, with respect to the optimization horizon, an optimal operation solution, based on the first charging demand, the second charging demand, and the charging demand forecast, wherein the optimal operation solution specifies: (1) for the electric vehicle arriving at the beginning of the current time step, whether the electric vehicle is assigned to a specific one of the first number of fixed chargers, or is added into a service queue of the second number of mobile devices, (2) for each time step in the optimization horizon, which one or more electric vehicles in the service queue are plugged and/or unplugged by the second number of mobile devices, and (3) foreach time step in the optimization horizon, respective charging power values to be applied by the first number of fixed chargers and the second number of mobile devices; by the operation management apparatus, sending respective information specified by the optimal operation solution to the electric vehicle arriving at the beginning of the current time step, the first number of fixed chargers, and the second number of mobile devices: by the first number of fixed chargers, charging electric vehicles assigned thereto, based on the specified charging power values to be applied by the first number of fixed chargers in the current time step; and by the second number of mobile devices, moving among electric vehicles in the service queue to perform plugging and/or unplugging, thereby charging the electric vehicles in the service queue based on the specified charging power values to be applied by the second number of mobile devices in the current time step.
The examiner submits that the foregoing bolded limitation(s) constitute an abstract idea because under its broadest reasonable interpretation, the claim covers a mental process and mathematical concept.
“obtaining…”, “generating…”, “solving…” recite abstract ideas - namely, mental processes that could be performed by a human with a pen and paper, per the MPEP, merely adapting them into the context of a technological environment with computing parts does not preclude them from being abstract. Furthermore, the idea of “solving…an optimal operation solution” corresponds to a mathematical concept. Namely a mathematical calculation. On Page 7 of Applicant’s specification, looking at Lines 8-12, the optimization problem is clearly framed as a mathematical optimization problem, specifically a “mixed-integer linear programming (MILP) problem”. As outlined above in Response to Arguments, we consider the broadest reasonable interpretation of effecting the method to encompass instructing a human operator in light of Page 5, Lines 27-31 of Applicant’s specification, thereby reciting a Certain Method of Organizing Human Activity.
Accordingly, the claim recites at least one abstract idea.
Claims 2-9 recite an abstract idea by virtue of their dependency from Claim 1.
Claim 10 recite abstract ideas by presenting substantially similar limitations.
Claims 11-18 recite abstract ideas by virtue of their dependency from Claims 10.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the MPEP, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into practical application. As noted in the MPEP, it must be determined whether any additional elements in the claim beyond the judicial exception integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements, such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method for operating an electric vehicle charging station, the charging station comprising a first number of fixed chargers, a second number of mobile devices, and an operation management apparatus, each of the mobile devices being configured to move in the charging station to plug and unplug an electric vehicle, the method comprising: at a current time step within an optimization horizon including the current time step and a plurality of subsequent time steps, by the operation management apparatus, obtaining, upon receiving a charging request from an electric vehicle arriving at a beginning of the current time step, a first charging demand; by the operation management apparatus, deriving, upon receiving charging dynamics of an electric vehicle having been staying at the charging station before the current time step, a second charging demand; by the operation management apparatus, generating, with respect to the optimization horizon a charging demand forecast; by the operation management apparatus, solving, with respect to the optimization horizon, an optimal operation solution, based on the first charging demand, the second charging demand, and the charging demand forecast, wherein the optimal operation solution specifies: (1) for the electric vehicle arriving at the beginning of the current time step, whether the electric vehicle is assigned to a specific one of the first number of fixed chargers, or is added into a service queue of the second number of mobile devices, (2) for each time step in the optimization horizon, which one or more electric vehicles in the service queue are plugged and/or unplugged by the second number of mobile devices, and (3) foreach time step in the optimization horizon, respective charging power values to be applied by the first number of fixed chargers and the second number of mobile devices; by the operation management apparatus, sending respective information specified by the optimal operation solution to the electric vehicle arriving at the beginning of the current time step, the first number of fixed chargers, and the second number of mobile devices: by the first number of fixed chargers, charging electric vehicles assigned thereto, based on the specified charging power values to be applied by the first number of fixed chargers in the current time step; and by the second number of mobile devices, moving among electric vehicles in the service queue to perform plugging and/or unplugging, thereby charging the electric vehicles in the service queue based on the specified charging power values to be applied by the second number of mobile devices in the current time step.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
As it pertains to Claim 1, the additional elements in the claims include “operating an electric vehicle charging station…”, “an operation management apparatus”, “fixed chargers…”, “mobile devices”…, “a charging request from an electric vehicle…”, “an electric vehicle…”. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional elements are generic computing components that are merely used as a tool to perform the recited abstract idea and/or do no more than generally link the use of the recited abstract idea to a particular technological environment or field of use under Step 2A Prong Two.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing an abstract idea.
Claim 10 additionally recites “an apparatus comprising a processor and a non-transitory memory storing instructions executable by the processor”.
These do not integrate the abstract idea into a practical application by analogous reasoning as outlined above.
Claims 2-9, 11-18 do not recite any additional limitations beyond those inherited from Claims they are dependent on.
101 Analysis – Step 2B
Regarding Step 2B of the MPEP, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to generic computing components that are merely used
as a tool to perform the recited abstract idea and/or do no more than
generally link the use of the recited abstract idea to a particular
technological environment or field of use. Further, looking at the additional
elements as an ordered combination adds nothing that is not already
present when considering the additional elements individually.
Claim 10 are rejected as disclosing substantially similar limitations as Claim 1.
Claims 10 recites additional limitations which merely further limit the abstract ideas of Claim 1, and are therefore ineligible. Additional limitations disclosed are as follows:
Claim 10 additionally recites “an apparatus comprising a processor and a non-transitory memory storing instructions executable by the processor”.
For analogous reasoning as above, Claim 10 does not integrate the abstract ideas recited into a particular application per Step 2A Prong II or amount to significantly more under Step 2B.
Claims 2-9,11-18 do not recite any additional elements beyond those recited in the claims from which they depend, and as a result, Claims 2-9,11-18 do not include any additional elements that either integrate under Step 2A Prong II or amount to significantly more under Step 2B.
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 (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 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, 5-7, 10-11, 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Starns(US 20190205842 A1) in view of Lee(WO 2019109084 A1).
Claims 1, 10, 19
As to Claim 1,
Starns teaches:
A method for operating an electric vehicle charging station, the charging station comprising a first number of fixed chargers a second number of mobile devices, and an operation management apparatus, each of the mobile devices being configured to move in the charging station to plug and unplug an electric vehicle,
In [0014], "AVs may periodically be recalled to a service facility with a fixed location for routine maintenance (e.g., charging of rechargeable batteries and software upgrades)". In [0015], "One or more service vehicles with portable electric charging stations may be sent to the mobile charging location to facilitate recharging the AV in the field. Other AVs may be informed of the parking location and may meet the portable electric charging station for charging". In [0017], "Once the AV has navigated to a particular charging station that it has been assigned, as described in more detail below, the AV may be connected to its assigned charging station for charging". In accordance with the broadest reasonable interpretation of the claim, we construe the mobile charging location to overlap with the facility. See [0013] regarding the arrangement of a service facility management system, analogizing to an operation management apparatus.
(1) for the electric vehicle arriving at the beginning of the current time step, whether the electric vehicle is assigned to a specific one of the first number of fixed chargers, or is added into a service queue of the second number of mobile devices,
In [0045], "In particular embodiments, transportation management system 410 may determine that AV 140 should be serviced in the “field” by charging service vehicle 302, rather than instructed to navigate to a service facility at a fixed location. As an example and not by way of limitation, the determination to service AV 140 in the field or navigate AV 140 to the service facility may be based on the service required by AV 140, time differences to complete the service in the field or at the service facility, or costs (e.g., comparing actual cost to perform the service and cost of time and non-revenue miles for the car to move to a service facility as compared to servicing in the field by a mobile vehicle 302)".
(2) for each time step in the optimization horizon, which one or more electric vehicles in the service queue are plugged and/or unplugged by the second number of mobile devices,
In [0033], " In particular embodiments, telemetry data may be collected by autonomous vehicle status monitor 434. For example, autonomous vehicle status monitor 434 may record information associated with utilization of AV 140 or charging service vehicle 302...For example, autonomous vehicle information may be collected from AV 140 or charging service vehicle 302 itself (e.g., by a controller area network bus) or from application programming interfaces provided by a vehicle manufacturer, which may send data directly to an in-car console and/or to transportation management system 410". In accordance with the broadest reasonable interpretation of the claim, we construe the utilization of a mobile service charging vehicle to analogize to the status of the vehicle being plugged or unplugged.
by the operation management apparatus, sending respective information specified by the optimal operation solution to the electric vehicle arriving at the beginning of the current time step, the first number of fixed chargers, and the second number of mobile devices:
In [0046], we communicate optimal logistics to the electric vehicle, “In particular embodiments, logistics module 417 of transportation management system 410 may determine whether AV 140 should be serviced in the “field” or instructed to navigate to a service facility based on a predicted demand and demand duration for AVs 140”. In [0045], we offer instructions to mobile units to complete the requested repair, “As described above, transportation management system 410 may provide instructions to charging service vehicle 302. As an example and not by way of limitation, transportation management system 410 may determine that the service required by AV 140 may be fulfilled by a charging service vehicle 302, and that it would be faster for AV to get serviced by charging service vehicle 302 instead of recalling AV 140 to a service facility”. In [0019], we consider the storing of assignment information of vehicles to charging locations to teach conveying salient information to the fixed station, “FIG. 2A illustrates a service facility at a fixed location. As described above, one of the regions within service facility 200 may be configured with a number of charging stations 202 for charging the batteries of AVs 140. In particular embodiments, AV 140 may be assigned to a particular charging station 202 by a computer system associated with service facility 200. AVs 140 may be assigned to their particular charging stations based on an anticipated charge time (or fuel level), so that AVs 140 requiring the least anticipated amount of charge time may be assigned to a charging station 202 closer to exit way 106”.
by the first number of fixed chargers, charging electric vehicles assigned thereto, … by the first number of fixed chargers in the current time step; and by the second number of mobile devices, moving among electric vehicles in the service queue to perform plugging and/or unplugging, thereby charging the electric vehicles in the service queue … by the second number of mobile devices in the current time step.
In [0014], "AVs may periodically be recalled to a service facility with a fixed location for routine maintenance (e.g., charging of rechargeable batteries and software upgrades)". In [0015], "One or more service vehicles with portable electric charging stations may be sent to the mobile charging location to facilitate recharging the AV in the field. Other AVs may be informed of the parking location and may meet the portable electric charging station for charging". In [0017], "Once the AV has navigated to a particular charging station that it has been assigned, as described in more detail below, the AV may be connected to its assigned charging station for charging". In accordance with the broadest reasonable interpretation of the claim, we construe the mobile charging location to overlap with the facility.
Starns does not expressly disclose the remaining limitations.
However, Lee teaches:
the method comprising: at a current time step within an optimization horizon including the current time step and a plurality of subsequent time steps, by the operation management apparatus, obtaining, upon receiving a charging request from an electric vehicle arriving at a beginning of the current time step, a first charging demand: by the operation management apparatus, deriving, upon receiving charging dynamics of an electric vehicle having beenstaying at the charging station before the current time step, a second charging demand; by the operation management apparatus, generating, with respect to the optimization horizon, a charging demand forecast;
In [0077], "Optimization horizon. Fix a time horizon T { 1 , 2. , 7.sup.' [. This defines a rolling time window over which EV charging rates can be optimized repeatedly, as in model-predictive control. Specifically, at time t, ACN:1 . assumes there will be no future EV arrivals (this is the online aspect);2. computes the charging rate for ever}.Math.’ EV that is active (e.g., still needs charging), by optimizing the charging rates of ail EVs over the time horizon t -f 1. , t + 2 + T". 3. for the period t + 1. charge all the active EVs at the calculated rates r(f -j~ 1 ) (n(l: ÷ 1 }. tor all EV i);4. updates the .remaining energy demand of each BY, and possibly other state variables at the end of time t 1 ;5. updates new EV arrivals in time 1 - 1. if any; and repeats the procedure at time i r 1. We consider the operation management apparatus to be the centralized computing system as outlined in [0006].
by the operation management apparatus, solving, with respect to the optimization horizon, an optimal operationsolution, based on the first charging demand, the second charging demand, and the charging demand forecast, ,
In [0088], "Many embodiments use a quadratic program to compute the charging rates for a set of EVs over a time period. A QP framework for computing charging rates for an EV in accordance with several embodiments of the invention is illustrated in Fig. 4". In [0089], "Although Fig 4 illustrate s a particular QF framework for computing charging rates for an EV, any of a variety of frameworks, including linear program with linear constraints, quadratic program with linear constraints, and quadratic program with quadratic constraints may be utilized as appropriate to the requirements of specific applications i n accordance with several embodiments of the invention. Capacity constraints for objective functions in accordance with numerous embodiments of invention is now described".
wherein the optimal operation solution specifies: … (3) for each time step in the optimization horizon, respective charging power values to be applied by the first number of fixed chargers and the second number of mobile devices;
In [0088], "Many embodiments use a quadratic program to compute the charging rates for a set of EVs over a time period. A QP framework for computing charging rates for an EV in accordance with several embodiments of the invention is illustrated in Fig. 4". In [0089], "Although Fig 4 illustrate s a particular QF framework for computing charging rates for an EV, any of a variety of frameworks, including linear program with linear constraints, quadratic program with linear constraints, and quadratic program with quadratic constraints may be utilized as appropriate to the requirements of specific applications in accordance with several embodiments of the invention. Capacity constraints for objective functions in accordance with numerous embodiments of invention is now described".
… based on the specified charging power values to be applied … in the current time step, …based on the specified charging power values to be applied … in the current time step
As outlined above, see [0088] for the determination of said values in an optimal operating solution.
Lee discloses a system for adaptively optimizing the charging of electric vehicles. Starns discloses a system meant to coordinate charging across a fleet of autonomous vehicles. Each reference discloses means for administering the charging of electrical vehicles. Extending the optimization mechanics of Lee is applicable to Starns as both are fundamentally concerned with managing electric vehicle charging.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the optimization mechanics as taught in Lee and apply that to the system of Starns. Motivation to do so comes from the fact that the claim is plainly directed to the predictable result of combining known items in the prior art, with the expected benefit that adopting the optimization mechanics would enable users to more efficiently coordinate the charging of electric vehicles.
Claims 10,19 are rejected as disclosing substantially similar limitations as Claim 1.
Claim 10 additionally recites “an apparatus comprising a processor and a non-transitory memory storing instructions executable by the processor”.
These limitations can be found in [0066] of Starns, “In particular embodiments, computer system 700 includes a processor 702, memory 704, storage 706, an input/output (I/O) interface 708, a communication interface 710, and a bus 712. Although this disclosure describes and illustrates a particular computer system having a particular number of particular components in a particular arrangement, this disclosure contemplates any suitable computer system having any suitable number of any suitable components in any suitable arrangement”.
Claims 2, 11
As to Claim 2, Starns combined with Lee teaches all the limitations of Claim 1 as discussed above.
Starns teaches:
The method of claim 1, wherein the second number of mobile devices are robotic chargers configured to move in the charging station to plug and/or unplug the electric vehicles in the service queue.
In [0015], "One or more service vehicles with portable electric charging stations may be sent to the mobile charging location to facilitate recharging the AV in the field. Other AVs may be informed of the parking location and may meet the portable electric charging station for charging". In [0017], "Once the AV has navigated to a particular charging station that it has been assigned, as described in more detail below, the AV may be connected to its assigned charging station for charging". In accordance with the broadest reasonable interpretation of the claim, we construe the mobile charging location to overlap with the facility. See [0026] regarding the mobile device being a robotic charger, “In particular embodiments, charging service vehicle 302 may be an autonomous vehicle that is configured navigate itself to different charging locations”. In accordance with the broadest reasonable interpretation of the claim, we consider an autonomous vehicle to teach the robotic functionality.
Claim 11 is rejected as disclosing substantially similar limitations as Claim 2.
Claims 5, 14
As to Claim 5, Starns combined with Lee teaches all the limitations of Claim 1 as outlined above.
Starns does not expressly disclose the remaining limitations.
However, Lee teaches:
The method of claim 1, wherein the method is performed in an iterative manner at each of the plurality of subsequent time steps to apply a receding horizon control
It would be well known to one of ordinary skill in the art that receding horizon control is alternatively known as model predictive control, in [0077], "Optimization horizon. Fix a time horizon T { 1 , 2. , 7.sup.' [. This defines a rolling time window over which EV charging rates can be optimized repeatedly, as in model-predictive control. Specifically, at time t, ACN:1 . assumes there will be no future EV arrivals (this is the online aspect);2. computes the charging rate for ever}.Math.’ EV that is active (e.g., still needs charging), by optimizing the charging rates of ail EVs over the time horizon t -f 1. , t + 2 + T". 3. for the period t + 1. charge all the active EVs at the calculated rates r(f -j~ 1 ) (n(l: ÷ 1 }. tor all EV i);4. updates the .remaining energy demand of each BY, and possibly other state variables at the end of time t 1 ;5. updates new EV arrivals in time 1 - 1. if any; and repeats the procedure at time i r 1.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the optimization mechanics analysis of Lee and apply that to the system of Starns. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claim 14 is rejected as disclosing substantially similar limitations as Claim 5.
Claims 6, 15
As to Claim 6, Starns combined with Lee teaches all the limitations of Claim 1 as discussed above.
Starns does not expressly disclose the remaining limitations.
However, Lee teaches:
The method of claim 1, wherein one or more time steps at an end of the optimization horizon, have a longer duration than one or more time steps at a beginning of the optimization horizon.
Viewing the one or more time steps as intervals where a service is being rendered, we consider this to be disclosed by the different service rates and durations accorded to different charging groups. With respect to the delayed duration of lower priority groups in [0145], “in many embodiments, the defining feature of Group I may be that EV i will be guaranteed its requested energy e.sub.s (up to a measurement error margin). This is in contrast with Group 2 that may guarantee only a minimum energ ¾.Math. > 0, but not the requested energy a. In order to guarantee .sub.<?,.Math., a key design decision is:
Group 1 has strict priority over Group 2 and Group 3.
This means that all resources will be devoted to satisfy Group 1 BV's energy requests e; first. Group 2 EVs will be allocated left-over capacities. Specifically the charging rates of Group 1 and Group 2 EVs are determined sequentially:
1. Solve QP {with equality energy constraints) for Group 1 EVs only. Denote their rates by has (>'.sup.*(/),! - Group 1./ { 1 , 7.sup.'j ) .
2. Compute left-over capacities for Group 2: [AltContent: rect] where i (t) are the original resource capacities 3. Solve QP (with inequality energy constraints) for Group 2 EVs only using the left-over capacities”. With respect to the altered charging rates, also affecting service rendering times in [0062], “Several embodiments of the QP may provide for priority charging among EVs by using an appropriate choice of parameter values. In particular, a higher priority EV can be assigned a larger weight, a larger minimum energy, and/or a larger maximum charging rate. In certain embodiments, a driver of an EV vehicle may pay a different price for prioritized charging”.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the optimization mechanics analysis of Lee and apply that to the system of Starns. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claim 15 is rejected as disclosing substantially similar limitations as Claim 6.
Claims 7,16
As to Claim 7, Starns combined with Lee teaches all the limitations of Claim 1 as discussed above.
Starns does not expressly disclose the remaining limitations.
However, Lee teaches:
The method of claim 1, wherein the generating step further comprises generating the charging demand forecast based on historical charging event data collected at the charging station
In [0235], "Accordingly, many embodiments provide a unified algorithmic framework to guide the design of a dean, flexible an evolvable architecture to implement various optimization- based product features. Fig. 26 illustrates building blocks of optimization-based product features in accordance with various embodiments of the invention. In particular, these may include;* User account and management, e.g., for priority or payment purposes;* Background load /,(/) real-time measurement and forecast from historical data;* Interface for DR commands !>(/ ) from site operator or utility;* User priority determination;* Current peak load measurement /..sup.)(0};* Interface for real-time prices />(/) and forecast from historical data;* Solar generation S(l } real-time measurement and forecast from historical data".
and/or input parameters from an operator of the charging station.
In [0075], " In a number of embodiments, the ACS controller and/or the ACS includes a touch screen display that enables the operator of an BV to provide information concerning the B.V connected to at) AC'S and/or information concerning desired charging requirements e.g., information indicative of a power requirement and an associated charging time such as (but. not limited to) departure time and/or desired additional miles to add to range of EV}.".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the optimization mechanics analysis of Lee and apply that to the system of Starns. Motivation to do so comes from the same rationale as outlined above with respect to Claim 1.
Claim 16 is rejected as disclosing substantially similar limitations as Claim 7.
Claims 3-4, 8-9, 12-13, 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Starns(US 20190205842 A1) in view of Lee(WO 2019109084 A1) in further view of Kumar(WO 2019126806 A1).
Claims 3,12
As to Claim 3, Starns combined with Lee teaches all the limitations of Claim 1 as discussed above.
Starns does not expressly disclose the remaining limitations.
However, Lee teaches:
the optimization horizon.
In [0077], "Optimization horizon. Fix a time horizon T { 1 , 2. , 7.sup.' [. This defines a rolling time window over which EV charging rates can be optimized repeatedly, as in model-predictive control. Specifically, at time t, ACN:1 . assumes there will be no future EV arrivals (this is the online aspect);2. computes the charging rate for ever}.Math.’ EV that is active (e.g., still needs charging), by optimizing the charging rates of ail EVs over the time horizon t -f 1. , t + 2 + T". 3. for the period t + 1. charge all the active EVs at the calculated rates r(f -j~ 1 ) (n(l: ÷ 1 }. tor all EV i);4. updates the .remaining energy demand of each BY, and possibly other state variables at the end of time t t 1 ;5. updates new EV arrivals in time 1 - 1. if any; and repeats the procedure at time i r 1.
Starns combined with Lee does not expressly disclose the remaining limitations.
However, Kumar teaches:
The method of claim 1, wherein the solving step further comprises solving the optimal operation solution such that an operation cost of the charging station is minimized with respect to
In [0055], "FIG. 5 illustrates an example embodiment of optimal planner flow 30 for operation of optimal planning module 18 in accordance with the present description. The optimal planner performs the optimization method(algorithm) for generating an optimal design and planning of the EV charging station together with DERs." In [0059], "he optimization problem is configured with an optimization function also called the objective function shown in block 42 of FIG. 5. In one embodiment, the objective function 42 comprises a weighted sum of annualized capital costs plus operational cost and also considers end user convenience (interchange/transition costs and time)...In at least one embodiment, the weights for these objective functions can be adjusted by the user".
Kumar discloses a system for optimizing the design of electric vehicle charging infrastructure. Starns combined with Lee discloses a system meant to adaptively optimize the charging of electric vehicles. Each reference discloses means for optimally administering the charging of electric vehicles. Extending the cost analysis as recorded in Kumar is applicable to the system of Starns combined with Lee is applicable as both are pertained to the problem of managing the charging of electric vehicles.
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the infrastructure cost analysis as taught in Kumar and apply that to the system as taught in Starns combined with Lee. Motivation to do so comes from the fact that the claim is plainly directed to the predictable result of combining known items in the prior art, with the expected benefit that adopting the cost analysis would enable operators of charging stations to optimize the configuration of their infrastructure.
Claim 12 is rejected as disclosing substantially similar limitations as Claim 3.
Claims 4,13
As to Claim 4, Starns combined with Lee and Kumar teaches all the limitations of Claim 3 as discussed above.
Starns combined with Lee does not expressly disclose the remaining limitations.
However, Kumar teaches:
The method of claim 3, wherein the operation cost is calculated based on an income from a charging fee,
In [0049], "Suggested pricing schemes account for the charger utilization prices that can be imposed by the facility to generate a revenue stream".
an expense for grid energy consumption,
Above [0078] Eq.10 factors in the cost of energy in providing the service to users.
a demand charge, a penalty on disappointment of a customer on a charging service, and/or a penalty on an unnecessary plugging and/or unplugging of an electric vehicle.
In [00157], " (iv) generating a recommendation from the optimal planning module for the custom EV charging infrastructure based on one or more factors comprising: charging needs, behavior of EV end users, interchange time and cost, instances of impatience, waiting time, and penalty for unfulfillment both at an aggregated demand level and at an individual vehicle level".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the infrastructure cost analysis of Kumar and apply that to the system of Starns combined with Lee. Motivation to do so comes from the same rationale as outlined above with respect to Claim 3.
Claim 13 is rejected as disclosing substantially similar limitations as Claim 4.
Claims 8, 17
As to Claim 8, Starns combined with Lee teaches all the limitations of Claim 1 as discussed above.
Starns teaches:
mobile devices
In [0015], "One or more service vehicles with portable electric charging stations may be sent to the mobile charging location to facilitate recharging the AV in the field. Other AVs may be informed of the parking location and may meet the portable electric charging station for charging".
Starns combined with Lee does not expressly disclose the remaining limitations.
However, Kumar teaches:
The method of claim I, further comprising, by the operation management apparatus:calculating, for each of a plurality of combinations of the first number of fixed chargers and the second number of ..., a total cost of ownership, based on a capital expense corresponding to said each combination and an operation expense corresponding to said each combination
In [0040], " Additional inputs 16 to optimal planner may comprise infrastructure facility data describing the variables associated with a facility (for example...umber and rating of existing EV chargers (if any), electrical infrastructure limits, facility loads, and number and size of existing Distributed Energy Resources (DER). Rating refers to the type of EV chargers used (for example, Level 2, Level 3, etc.)". In [00142], "The optimization problem is configured to have an optimization function also called the objective function; the objective function can be a weighted sum of annualized capital costs and the end user convenience. The annualized capital costs include the annualized capital cost of DERs, EV charging stations, any infrastructure upgrades." For the operation management apparatus, see [0025], “These inputs 16 are made available to the optimal planning module 18, which generates outputs 20 that are directed through the human interaction layer 14b for use by the user 12’.
And determining an optimal combination of the first number of fixed chargers and the second number of ... such that the corresponding total cost of ownership is minimized.
In [0055], "FIG. 5 illustrates an example embodiment of optimal planner flow 30 for operation of optimal planning module 18 in accordance with the present description. The optimal planner performs the optimization method(algorithm) for generating an optimal design and planning of the EV charging station together with DERs." In [0059], "he optimization problem is configured with an optimization function also called the objective function shown in block 42 of FIG. 5. In one embodiment, the objective function 42 comprises a weighted sum of annualized capital costs plus operational cost and also considers end user convenience (interchange/transition costs and time)...In at least one embodiment, the weights for these objective functions can be adjusted by the user".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the infrastructure cost analysis of Kumar and apply that to the system of Starns combined with Lee. Motivation to do so comes from the same rationale as outlined above with respect to Claim 3.
Claim 17 is rejected as disclosing substantially similar limitations as Claim 8.
Claims 9, 18
As to Claim 9, Starns combined with Lee teaches all the limitations of Claim 1 as discussed above.
Starns teaches:
mobile devices
In [0015], "One or more service vehicles with portable electric charging stations may be sent to the mobile charging location to facilitate recharging the AV in the field. Other AVs may be informed of the parking location and may meet the portable electric charging station for charging".
Starns combined with Lee does not expressly disclose the remaining limitations.
However, Kumar teaches:
The method of claim 1, further comprising, by the operation management apparatus:calculating, with respect to the first number of fixed chargers and the second number of …, a capital expense and an operation expense;
In [0040], " Additional inputs 16 to optimal planner may comprise infrastructure facility data describing the variables associated with a facility (for example...number and rating of existing EV chargers (if any), electrical infrastructure limits, facility loads, and number and size of existing Distributed Energy Resources (DER). Rating refers to the type of EV chargers used (for example, Level 2, Level 3, etc.)". In [00142], "The optimization problem is configured to have an optimization function also called the objective function; the objective function can be a weighted sum of annualized capital costs and the end user convenience. The annualized capital costs include the annualized capital cost of DERs, EV charging stations, any infrastructure upgrades". For the operation management apparatus, see [0025], “These inputs 16 are made available to the optimal planning module 18, which generates outputs 20 that are directed through the human interaction layer 14b for use by the user 12’.
keeping the capital expense unchanged
In [0077], "Similarly, the financial constraints like limits on budget spending needs to be enforced. Since these limits are different for each facility, the limits are keyed in as inputs by each facility. Financial constraints also include cost recovery from users, the market prices for EV charging, to ensure the competitiveness of the project". We understand the fixing of a constraint such as capital expense to be well known to one of ordinary skill in the art of routine statistical methods such as those outlined in [00127], "The long-term planner 64 uses output from the long-term forecasting module 88 and data from the data block 100 (static 90 and operations data 92) to generate planning spanning months to years ahead. In one embodiment, the long-term planner 64 uses Mixed Inter Linear programming or Mixed Integer Conic Programming using inputs from the long-term forecasting module 88, infrastructure data and operations strategy data.".
and replacing one or more of the first numberof fixed chargers with at !east one to calculate a corresponding operation expense; and determining an optimal combination of the first number of fixed chargers and the second number of ... such that the corresponding operation expense is minimized.
The optimization algorithm implicitly calculates an operational expense with respect to charger types in [0040], " Additional inputs 16 to optimal planner may comprise infrastructure facility data describing the variables associated with a facility (for example...number and rating of existing EV chargers (if any), electrical infrastructure limits, facility loads, and number and size of existing Distributed Energy Resources (DER). Rating refers to the type of EV chargers used (for example, Level 2, Level 3, etc.)" This can be calibrated towards operational expense in [0059], "The optimization problem is configured with an optimization function also called the objective function shown in block 42 of FIG. 5. In one embodiment, the objective function 42 comprises a weighted sum of annualized capital costs plus operational cost and also considers end user convenience (interchange/transition costs and time)...In at least one embodiment, the weights for these objective functions can be adjusted by the user".
It would have been obvious to one having ordinary skill in the art at the effective filling date of the invention to apply the infrastructure cost analysis of Kumar and apply that to the system of Starns combined with Lee. Motivation to do so comes from the same rationale as outlined above with respect to Claim 3.
Claim 18 is rejected as disclosing substantially similar limitations as Claim 9.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THEODORE L XIE whose telephone number is (571)272-7102. The examiner can normally be reached M-F 9-5.
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, Rutao Wu can be reached at 571-272-6045. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/THEODORE XIE/Examiner, Art Unit 3623
/WILLIAM S BROCKINGTON III/Primary Examiner, Art Unit 3623