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 the Application
Claims 1, 3-8, 11-12, 14, and 16-25 have been examined in this application.
The filling date of this application number recited above is 06 June 2024. No priority has been claimed in the Application Data Sheet, thus the examination will be undertaken in consideration of the effective filing date as the priority date.
No additional information disclosure statement (IDS) has been filed to date.
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, 3-8, 11-12, 14, and 16-25 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. The Claims are directed to an abstract idea, Mental Process and/or Methods of Organizing Human Activity. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea.
As per Claims 1 and 12, the claims recite “a method for providing an electric vehicle charging payment comprising:
computing, via a [equation] configured to combine GNSS data and vehicle sensor data, an indication of at least a first location of an electric vehicle from a combination of the GNSS data and vehicle sensor data;
obtaining payment data for one or more electric vehicle charge points proximate to the first location of an end user device;
determining, via the [equation], a charge point interaction indicator based, at least in part, on the computed indication of first location and payment data for one or more electric vehicle charge points proximate to the first location before the electric vehicle arrives at one of the electric vehicle charge points;
transmitting at least one charge payment data to a charge point, via a [communication], wherein the at least one charge payment data transmitted to the charge point is estimated based on electrical vehicle battery data; and
generating an automated vehicle control in response to the charge point interaction indicator and the transmitted charge payment data, wherein the automated vehicle control navigates the electric vehicle along a route to the charge point.”
The limitation of the claims recited above, considering the claims without the additional elements (e.g. computer, processor, computer program code, etc.), under its broadest reasonable interpretation (BRI), recites Mental Process. The method recited above is a process of data analysis with the steps of computing data, determining data based on the obtained data (e.g. comparing data), transmitting data, and generating data.
In view of the specification, the “charge point interaction indicator” is a prediction or likelihood an end user will interact with or enter a given charge point (Spec [0088]-[0089]). A human mind may predict based on the current location and what they know of payment data for a proximate vehicle charge point the likelihood of the user interacting or entering the charge point, before the user arrives at the charge point. For example, a user driving a specific highway can recall that the nearby charging stations do not accept debt cards or only one charging station does accept it, which is the user’s current form of payment, and thereby predicts it is likely that the user will not stop at the stations that do not accept the user’s form of payment and/or will likely stop at the only station that accepts it.
The user may mentally estimate the charge payment data based on electric vehicle battery data, as merely general calculation of charge cost to how far the battery information is displayed to be decreased, similar to how a person can prepay for gas needed based on observing the fuel gage.
These steps can be carried out in the human mind and/or by a human with pen and paper, which is a mental process, as disclosed by MPEP 2106.04(a)(2)(III):
“The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation. See, e.g., Benson, 409 U.S. at 67, 65, 175 USPQ at 674-75, 674 (noting that the claimed "conversion of [binary-coded decimal] numerals to pure binary numerals can be done mentally," i.e., "as a person would do it by head and hand."); Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1139, 120 USPQ2d 1473, 1474 (Fed. Cir. 2016) (holding that claims to a mental process of "translating a functional description of a logic circuit into a hardware component description of the logic circuit" are directed to an abstract idea, because the claims "read on an individual performing the claimed steps mentally or with pencil and paper").
Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, "[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind." Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer")”.
Additionally, the claims recite certain methods of organizing human activities, specifically under fundamental economic principles or practices. The claims recite steps associated with obtaining and transmitting payment data for a service (e.g. electric vehicle charge), wherein performing transaction is fundamental economic principles or practices. Therefore, the claims recite an abstract idea.
This judicial exception is not integrated into practical application. In particular, the claims recite an additional element of “apparatus”, “processor”, “memory”, and “computer program code” to perform the method recited above by instructing the abstract idea to be performed “by” these generic computer components. These general computer components are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer system. These additional elements are generic, off-the-shelf components available to the public, and does not require any specialized hardware or equipment to perform the claimed method, but are merely applied to perform its basic functionalities, such as: obtain data, determine data, transmit data, and generate data. Mere instructions to implement the abstract idea on a generic computer system, or merely using the generic computer system as a tool to perform the abstract idea (e.g. mere “apply it”) is not indicative of integration into a practical application; see MPEP 2106.05(f). Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to compute, determine, transmit, or generate data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., mental process or certain methods of organizing human activities) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Accordingly, these additional element do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims also recite additional element associated with “automated vehicle control”, as disclosed “generating an automated vehicle control in response to the charge point interaction indicator and the transmitted charge payment data, wherein the automated vehicle control navigates the electric vehicle along a route to the charge point”. As similarly discussed above, this additional element of automated electric vehicle function is merely applied to the abstract idea, in view of Specification:
[0080] “It should be noted that the sedan 56 in this example may represent any vehicle. Such vehicles may be standard gasoline powered vehicles, hybrid vehicles, an electric vehicle, a fuel cell vehicle, and/or any other mobility implement type of vehicle (e.g., bikes, scooters, etc.) … The vehicle may be a
non-autonomous vehicle, assisted driving vehicle, or an autonomous vehicle.”
which recites the vehicle may be applied to any type of standard vehicle, including generic automated vehicles with varying levels of autonomy. The automatic route guidance is described in a high-level means that one of ordinary skill would understand that this is merely applying the known autonomous route guidance to the abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Additionally, the claims recite steps associated with the data (i.e. obtain data, compute data, determine data, transmit data, and generate data), wherein mere data gathering and/or mere data manipulation is adding insignificant extra-solution activity to the judicial exception, which is also not indicative of integration into a practical application; see MPEP 2106.05(g). The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Here, the steps of obtaining data, determining data, transmitting data, and generating data to be used in the claimed process are incidental to the primary process of data analysis. 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.
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, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, the additional element of using a computer based system is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer system. The claims lack sufficient technical details to provide how these limitations may provide technological steps or technical details on how it is particularly implemented on a computer to improve its system or any of its underlying hardware or components (e.g. how it is performed on the computer, how it could improve the computer itself, how it could manipulate the computer to function in a specific way other than its generic functionality, and/or how it could improve any of the underlying technology), but merely applies the generic computer system to perform its generic functionalities. Merely using the generic computer system as a tool to perform the abstract idea (e.g. mere “apply it”) and/or adding insignificant extra-solution activity to the judicial exception (e.g. mere data gathering and/or data manipulation) is not indicative of an inventive concept (aka “significantly more”). In view of the Specification, the judicial exception is not applied with or used by a particular machine. As held in Parker v. Flook, 437 U.S. 584, 590, 198 USPQ 193, 199 (1978) and Bancorp Services v. Sun Life, 687 F.3d 1266, 1276, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012), “the routine use of a computer to perform calculations cannot turn an otherwise ineligible mathematical formula or law of nature into patentable subject matter.” The claims are not patent eligible.
Regarding dependent claims, they are still directed to an abstract idea without significantly more.
Claims 3 and 14 recite “wherein the at least one charge payment data transmitted to the charge point is dependent upon at least end user payment preference data.” The claims provide further details regarding the charge payment data, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 4 recites “wherein the at least one charge payment data transmitted to the charge point is dependent upon at least payment system metadata.” The claims provide further details regarding the charge payment data, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claims 5 and 16 recite “wherein the at least one charge payment data transmitted to the charge point is selected from a multitude of payment systems.” The claims provide further details regarding the charge payment data, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claims 6 and 17 recite “wherein the at least one charge payment data transmitted to the charge point includes at least a one-time use credit card number.” The claims provide further details regarding the charge payment data, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claims 7 and 18 recite “wherein the at least one charge payment data transmitted to the charge point includes at least a virtual credit card number.” The claims provide further details regarding the charge payment data, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claims 8 and 19 recite “wherein the payment data transmitted to the one or more electric vehicle charge points is based on region specific payment data.” The claims provide further details regarding the payment data, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claims 11 and 20 recite “wherein the at least one charge payment data transmitted to the charge point is transmitted from a mobile device.” The claims provide further details regarding the charge payment data, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 21 recites “further comprising: training a machine learning model to determine the charge point interaction indicator; providing a training dataset for training the machine learning model, wherein the training dataset includes one or more training examples, each training example including at least: (i) the first location of the electric vehicle and (ii) the payment data indicative of payment compatibility of the charge point.” The claim provides additional element “machine learning model”, wherein the machine learning model being provided training dataset to be trained is still a mere “apply it” of using the machine learning model as a mere “black-box” application, which is not indicative of integration into a practical application.
Claim 22 recites “wherein the one or more training examples includes data demonstrating recorded travel routine, and when the electric vehicle travels according to the recorded travel routine, a confidence score, acting as the charge point interaction indicator, is increased by the machine learning model.” The claim provides further details regarding the training dataset, which is still part of the abstract idea, and the machine learning model is merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 23 recites “wherein recorded travel routine is defined by a route and a timeframe.” The claim provides further details regarding the data, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 24 recites “wherein a confidence score of the charge point, acting as the charge point interaction indicator and computed via the computer program code, is increased when the battery level is dropped below a pre-determined level and no other charge points is located within a pre-determined range from the electric vehicle.” The claim provides further details regarding the data, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
Claim 25 recites “wherein receiving real-time data regarding availability of slots of the charge point, via an internet connection, generating the automated vehicle control in response to the availability of the slots of the charge point to navigate the electric vehicle along a detailed route to an available slot of the charge point.” The claim provides further details regarding the data, which is still part of the abstract idea, and the additional elements are merely applied to implement the abstract idea, which is not indicative of integration into a practical application.
These additional steps of each claims fail to remedy the deficiencies of their parent claim above because they are merely further limiting the rules used to conduct the previously recited abstract idea, and are therefore rejected for at least the same rationale as applied to their parent claim above.
Claims 3-8, 11, 14, and 16-25, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are sufficient to integrate into a practical application and do not amount to significantly more than the judicial exception. Similarly to the independent claim, each claim recites using a generic computer system to perform the abstract idea as mentioned above. Mere “apply it” is not “significantly more”. Therefore, prong 2 and step 2B analysis are similar to above and these claims are not eligible.
Therefore, Claims 1, 3-8, 11-12, 14, and 16-25 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 12, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Sabripour (US 20150306969 A1) in view of Ahmed et al. (US 20200372790 A1), in view of Bjorkengren (US 11827118 B1), and in view of Lee et al. (US 20210088992 A1).
As per Claims 1 and 12, Sabripour discloses a method for providing an electric vehicle charging payment comprising:
computing, via a computer program code configured to combine [GPS] data and vehicle sensor data, an indication of at least a first location of an electric vehicle from a combination of the [GPS] data and vehicle sensor data ([0034] “The map 306 may include icons indicating a location of available charging stations 302 and an icon indicating a current position 304 of the electric car 104 relative to the available charging stations 302” or see also [0045] “The memory 412 may further include a map application 516 that, when executed, causes the processor 504 to generate a roadmap including an indicator representing a current location of the computing system on the map (based on global positioning satellite (GPS) data provided by the computing system)”);
obtaining payment data for one or more electric vehicle charge points proximate to the first location of an end user device ([0045] “The memory 412 further includes billing instructions 518 that, when executed, cause the processor 504 to charge an account associated with the user to reserve a charging station”);
determining, via the computer program code, a charge point interaction indicator based, at least in part, on the computed indication of first location and payment data for one or more electric vehicle charge points proximate to the first location before the electric vehicle arrives at one of the electric vehicle charge points ([0052] “The electric car 104 may be provided with a computing system 208, such as an on-board navigation system, capable of interacting with the automobile charge scheduling system 102 to determine available charging stations, to reserve the charging station, and to receive directions to the selected (reserved) charging station for recharge. In an embodiment, a driver may pre-pay for a charging station and reserve a spot, which reservation and payment may be communicated between the computing system 208 and the automobile charge scheduling system 102 through the network 106”);
transmitting at least one charge payment data, via a wireless communication interface, to a charge point ([0052] “In an embodiment, a driver may pre-pay for a charging station and reserve a spot, which reservation and payment may be communicated between the computing system 208 and the automobile charge scheduling system 102 through the network 106”), …;
Although Sabripour teaches of determining the current position of the electric vehicle from the map application using GPS, the referenced prior art does not seem to explicitly disclose of combining GNSS data and vehicle sensor data to determine the location of the electric vehicle. However, Ahmed teaches:
computing, via a computer program code configured to combine GNSS data and vehicle sensor data, an indication of at least a first location of an electric vehicle from a combination of the GNSS data and vehicle sensor data ([0006] “A location and governance method for a light electric vehicle can include receiving positioning system position signals for the vehicle; computing an absolute position for the vehicle based on the GNSS position signals; receiving sensor readings associated with the vehicle from other sensors on-board the vehicle; computing a relative position for the vehicle based on the sensor readings associated with the vehicle; computing a determined vehicle position using the absolute position and the relative position computed for the vehicle”);
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the combination of vehicle sensor data and GNSS data to compute vehicle position as in Ahmed in the system executing the method of Sabripour with the motivation of offering to [0018] “improve the integer ambiguity resolution step performed in differential GNSS position solutions by adjusting the residuals of each potential integer set with the additional position constraints before resolving the ambiguities” as taught by Ahmed over that of Sabripour.
Although Sabripour teaches of providing pre-payment to a charging station for reservation, the referenced prior art does not seem to explicitly disclose of the charge payment data being estimated based on electrical vehicle battery data. However, Bjorkengren teaches:
transmitting at least one charge payment data, via a wireless communication interface, to a charge point, wherein the at least one charge payment data transmitted to the charge point is estimated based on electrical vehicle battery data (See Figure 2 – steps 202 to 222, as disclosed [Col 13 Lines 3-12] “In another example, the user could select a simple input which indicates the user's desire to charge the vehicle battery, and the quantity or energy for charging and funds amount are determined automatically. In other scenarios, method 200 could be initiated automatically in response to a charge level of the vehicle battery dropping below a minimum charge threshold, so that the user will be able to charge the vehicle battery even if they go out of a cellular or internet service area” and [Col 14 Lines 57-65] “At 222, user device 322 sends (by a communication interface 128) the first signed receipt to charge station 330. At 230, the first signed receipt is received at charge station 330 (by a communication interface 138). The first signed receipt is processed (by at least one processor 134 of charge station 330), and if the signature of the first signed receipt is successfully verified, energy is provided to the vehicle battery at 232 (via an energy couple 139 of charge station 330)”);
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the pre-payment to a charge station based on electrical vehicle battery data as in Bjorkengren in the system executing the method of Sabripour with the motivation of offering to [Col 20 Lines 8-25] prevent fraud as taught by Bjorkengren over that of Sabripour.
Although Sabripour teaches of reserving a charge station and providing pre-payment, the referenced prior art does not seem to explicitly disclose of an automated vehicle control to the charge point. However, Lee teaches:
generating an automated vehicle control in response to the charge point interaction indicator and the transmitted charge payment data, wherein the automated vehicle control navigates the electric vehicle along a route to the charge point ([0078] “Thus, the electric vehicle 10 may guide the route to the reserved (selected) charging station or may perform automatic driving control. To this end, the electric vehicle 10 may further include a navigation device (not shown) or an automatic driving controller (not shown)” wherein reserving the charging station also includes sending the payment information, as disclosed [0011] “In an embodiment, the processor may transmit one of … payment type information to the charging station management server, may request the charging station management server to perform one of the reservation of the charging station”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the automatic driving control to the reserved charging station as in Lee in the system executing the method of Sabripour with the motivation of offering to [0003-0007] reduce wait times and improve user convenience as taught by Lee over that of Sabripour.
As per Claim 25, Sabripour discloses the method according to claim 1, wherein receiving real-time data regarding availability of slots of the charge point, via an internet connection, generating the automated vehicle control in response to the availability of the slots of the charge point to navigate the electric vehicle along a detailed route to an available slot of the charge point ([0034] “In response to receiving list of available charging stations from the ACSS 102, the computing system 208 may present the list on a map 306 within the display console 210. The map 306 may include icons indicating a location of available charging stations 302 and an icon indicating a current position 304 of the electric car 104 relative to the available charging stations 302 … In the illustrated example, the audio alert includes the following statement and prompt: “There are two ‘available’ charging stations within twenty-five miles of your current location. Would you like me to reserve the closest available station?””).
Claims 4, 11, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sabripour, in view of Ahmed, in view of Bjorkengren, in view of Lee, and in view of Akhtar (US 20200333151 A1).
As per claim 4, Sabripour may not explicitly disclose, but Akhtar teaches the method according to Claim 1, wherein the at least one charge payment data transmitted to the charge point is dependent upon at least payment system metadata ([0033] “By using an IoT gateway client software at the charging pole which establishes direct connection to the ethereum blockchain in the back-end via GPS or WiFi and uses a smart phone app to communicate with the payment interface. When EV charge is initialized, the payment gateway is accessed and it gets processed when the charge is complete. The blockchain can manage and record all of the payment and charging data, a fully automated authentication ledger system which gives a solution for charging and billing without centralized system or middle man”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the payment system metadata as in Akhtar in the system executing the method of Sabripour with the motivation of offering to [Abstract] solve “charging unit downtime and loss of revenue by doing predictive maintenance so that EV charging station managers can schedule maintenance ahead of time” and provide “seamless and automated payment integration for secured, efficient and transparent payment process using the block chain” as taught by Akhtar over that of Sabripour.
As per claims 11 and 20, Sabripour may not explicitly disclose, but Akhtar teaches the method according to Claim 1, and the apparatus according to Claim 12, wherein the at least one charge payment data transmitted to the charge point is transmitted from a mobile device ([0016] “As soon as the User is done with charging their electric vehicle, the amount is calculated by the kilowatt of energy and the total amount is charged in dollars to User's credit card or by blockcharge using blockchain payment integration with secured payment gateway that is integrated into the mobile app”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize mobile app providing the payment data as in Akhtar in the system executing the method of Sabripour with the motivation of offering to [Abstract] solve “charging unit downtime and loss of revenue by doing predictive maintenance so that EV charging station managers can schedule maintenance ahead of time” and provide “seamless and automated payment integration for secured, efficient and transparent payment process using the block chain” as taught by Akhtar over that of Sabripour.
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Sabripour, in view of Ahmed, in view of Bjorkengren, in view of Lee, and in view of Ashby et al. (US 20190263271 A1).
As per claims 3 and 14, Sabripour may not explicitly disclose, but Ashby teaches the method according to Claim 1, and the apparatus according to Claim 12, wherein the at least one charge payment data transmitted to the charge point is dependent upon at least end user payment preference data ([0071] “For example, the primary EV 12 mobile wireless device may prompt the primary EV user to input … payment preferences … which may then be relayed to a service provider”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize payment preferences as in Ashby in the system executing the method of Sabripour with the motivation of offering to improve user experience by allowing the user to configure payment methods as taught by Ashby over that of Sabripour.
Claims 5-7 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Sabripour, in view of Ahmed, in view of Bjorkengren, in view of Lee, and in view of Ketharaju et al. (US 20210209566 A1).
As per claims 5 and 16, Sabripour may not explicitly disclose, but Ketharaju teaches the method according to Claim 1, and the apparatus according to Claim 12, wherein the at least one charge payment data transmitted to the charge point is selected from a multitude of payment systems ([0034] “Continuing with method 300, after the user is prompted at 306, a user selection may be received whether the alternate payment source information will override the default payment source or share payment with the default payment source at 310 … The user selection includes an indication of whether the alternate payment source is used to override the default payment source or share in the expenses with the default payment source. If the override option is chosen, the default payment source will be temporarily replaced with the alternate payment source at 312. If the share option is chosen, the default payment source will share future costs with the alternate payment source at 314”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize providing payment options to the user as in Ketharaju in the system executing the method of Sabripour with the motivation of offering to improve user experience and convenience for payments associated with the vehicle as taught by Ketharaju over that of Sabripour.
As per claims 6 and 17, Sabripour may not explicitly disclose, but Ketharaju teaches the method according to Claim 1, and the apparatus according to Claim 12, wherein the at least one charge payment data transmitted to the charge point includes at least a one-time use credit card number ([0004] “Alternately, the VPD may use a combination or sub-combination of default financial information and new financial information to create a one-time use virtual credit card number to provide to the merchant for payment for goods or services”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize one-time use credit card number for the transaction as in Ketharaju in the system executing the method of Sabripour with the motivation of offering to improve user experience and convenience for payments associated with the vehicle as taught by Ketharaju over that of Sabripour.
As per claims 7 and 18, Sabripour may not explicitly disclose, but Ketharaju teaches the method according to Claim 1, and the apparatus according to Claim 12, wherein the at least one charge payment data transmitted to the charge point includes at least a virtual credit card number (Claim 13 “The financial institution computing system of claim 12, wherein the virtual payment number is a virtual credit card number”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize virtual credit card number for the transaction as in Ketharaju in the system executing the method of Sabripour with the motivation of offering to improve user experience and convenience for payments associated with the vehicle as taught by Ketharaju over that of Sabripour.
Claims 8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Sabripour, in view of Ahmed, in view of Bjorkengren, in view of Lee, and in view of TAJIMA (US 20110161143 A1).
As per claims 8 and 19, Sabripour may not explicitly disclose, but TAJIMA teaches the method according to Claim 1, and the apparatus according to Claim 12, wherein the payment data transmitted to the one or more electric vehicle charge points is based on region specific payment data ([0054] “In the calculation in the new charging area, when the electric vehicle has traveled countries and regions with different tax rates, the controller 50 may calculate, assuming the charging area corresponding to the ratio of the travel distances of the countries and regions. Then, the controller 50 calculates a tax to be paid based on the tax rate of each location and, if necessary, pays the tax to the country or the region”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize regional tax data for EV charging as in TAJIMA in the system executing the method of Sabripour with the motivation of offering to improve user experience and convenience for automatically calculating the regional electricity tax data for payment as taught by TAJIMA over that of Sabripour.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Sabripour, in view of Ahmed, in view of Bjorkengren, in view of Lee, and in view of Bagalkoti et al. (US 20250289342 A1).
As per Claim 21, Sabripour may not explicitly disclose, but Bagalkoti discloses the method according to claim 1, further comprising:
training a machine learning model to determine the charge point interaction indicator ([0028] “Additionally, the charging service application 120 utilizes an algorithm to match EVs with charging stations. The algorithm may utilize techniques including: artificial intelligence; linear programming; operations research; and other techniques to match the EVs with appropriate charging stations”);
providing a training dataset for training the machine learning model, wherein the training dataset includes one or more training examples, each training example including at least: (i) the first location of the electric vehicle and (ii) the payment data indicative of payment compatibility of the charge point ([0028] “The algorithm may be operated in an iterative manner, as the locations of EVs change and as the charge (i.e., the percentage of charge capacity, the actual energy, or other measure) on batteries changes (in both moving EVs and charging EVs). The algorithm may also predict the future locations of the EVs and traffic conditions, to forecast the charging demand of one or more charging station locations more accurately” and [0037] “The charging station application 122 may also be configured to maintain a database 408 of information regarding EVs previously served, payment methods used, and other data as needed”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize AI algorithm to match EV to charging stations as in Bagalkoti in the system executing the method of Sabripour with the motivation of offering to provie more accurate and [0028] obtain better results as taught by Bagalkoti over that of Sabripour.
Claims 22 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Sabripour, in view of Ahmed, in view of Bjorkengren, in view of Lee, in view of Bagalkoti, and in view of Dufford (US 20140257608 A1).
As per Claim 22, Sabripour may not explicitly disclose, but Dufford discloses the method according to claim 21, wherein the one or more training examples includes data demonstrating recorded travel routine, and when the electric vehicle travels according to the recorded travel routine, a confidence score, acting as the charge point interaction indicator, is increased by the machine learning model ([0038] “FIG. 4 depicts a graph 400 showing how a confidence value may be used to scale the power adjustments. Utilizing location information from the GPS unit 180, the processor 150 compares the current location with routes stored in the route history. The processor 150 may further utilize updated location information as the hybrid vehicle 100 moves, to further determine a route from the route history that matches the current route. The processor 150 calculates a confidence value that indicates how close a match the current route is with a route in the route history”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize confidence value for machine learning based on matching route history as in Bagalkoti in the system executing the method of Sabripour with the motivation of offering to improve fuel (e.g. battery) efficiency as taught by Bagalkoti over that of Sabripour.
As per Claim 23, Sabripour may not explicitly disclose, but Dufford discloses the method according to claim 22, wherein recorded travel routine is defined by a route and a timeframe ([0030] “The route may further include information such as timestamps, time durations, distances, etc.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize timestamps or time durations included in the route history data as in Bagalkoti in the system executing the method of Sabripour with the motivation of offering to improve fuel (e.g. battery) efficiency as taught by Bagalkoti over that of Sabripour.
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Sabripour, in view of Ahmed, in view of Bjorkengren, in view of Lee, and in view of Uyeki (US 20120109519 A1).
As per Claim 24, Sabripour may not explicitly disclose, but Uyeki discloses the method according to claim 1, wherein a confidence score of the charge point, acting as the charge point interaction indicator and computed via the computer program code, is increased when the battery level is dropped below a pre-determined level and no other charge points is located within a pre-determined range from the electric vehicle ([0016] “The disclosure relates to battery electric vehicles and navigation systems and methods therefor in which charging station information is provided in a data store for access by BEV navigation systems to determine charging equipment locations, availability, and compatibility for intelligent routing decisions for charging the vehicle battery. If compatible charging stations are within range, the navigation system recommends one or more charging stations according to the availability and compatibility information and can construct a route for navigation of the BEV to a selected charging station, where the recommendation may take into account the total energy required for a round trip route to the charger location. The disclosure also contemplates use of information regarding expected future availability to enable provision of a recommendation if all in-range charging stations are currently in use” and see Figure 3B for calculating the candidate charging station).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize system to determine the most candidate charging station based on availability of nearby stations and battery SOC as in Uyeki in the system executing the method of Sabripour with the motivation of offering to provide [Abstract] “improved vehicle navigation systems and routing techniques for BEVs to facilitate routing the vehicle to charging equipment while mitigating power down situations” as taught by Uyeki over that of Sabripour.
Response to Arguments
Applicant’s arguments, see pages 7 to 11, filed 24-February-2026, with respect to 35 U.S.C. 101 rejection have been fully considered but they are not persuasive, as Applicant is arguing for certain features of the invention that are not claimed.
Applicant contents it is not possible for the human mind to compute an indication of at least a first location of an electric vehicle from a combination of GNSS data and vehicle sensor data, and cites to the more specific details of the specification [0051], however the analysis of traffic or security cameras with GNSS and vehicle sensor data, via machine learning, is not claimed; nor is multi-sensor fusion. The claim does not positively recite acquiring the sensor data from the vehicle sensors or GNSS. A person can determine a first location (generally) based on received GNSS data and mileage data of a vehicle. The specific elements of a GNSS receiver (not claimed) and vehicle sensor (not claimed) would be additional elements. The specific limitation claimed is a computer program code which is recited at a high level and amounts to no more than merely applying a generic computer to execute the code.
The limitations of “obtaining payment data for one or more electric vehicle charge points” can also be performed mentally as looking-up in a memory the payment data of a nearby charge point. In view of the specification, the “charge point interaction indicator” is a prediction or likelihood an end user will interact with or enter a given charge point (Spec [0088]-[0089]). A human mind may predict based on the current location and what they know of payment data for a proximate vehicle charge point the likelihood of the user interacting or entering the charge point, before the user arrives at the charge point. For example, a user driving a specific highway can recall that the nearby charging stations do not accept debt cards or only one charging station does accept it, which is the user’s current form of payment, and thereby predicts it is likely that the user will not stop at the stations that do not accept the user’s form of payment and/or will likely stop at the only station that accepts it.
The user may mentally estimate the charge payment data based on electric vehicle battery data, as merely general calculation of charge cost to how far the battery information is displayed to be decreased, similar to how a person can prepay for gas needed based on observing the fuel gage. Applicant’s arguments that the recitation requires batter management telemetry is not required by the high level recitation of the actual claim language. The limitation of transmitting the payment data over a network is an additional element. However, as explained under Step 2A Prong 2 is merely extra solution activity. Further under Step 2B, this is supported by the courts as well-understood, routine and conventional functions by computers (MPEP 2106.05(d)(II)(i)).
The limitation of “generating an automated vehicle control…”, wherein the automated vehicle control navigates the electric vehicle along a route to the charge point is considered an additional element. However, in light of the specification, this limitation is merely applying an automated electric vehicle function to the abstract idea. As described in Step 2B, the specification at [0080] states the vehicle may be applied to any type of standard vehicle, including generic automated vehicles with varying levels of autonomy. The automatic route guidance is described in a high-level means that one of ordinary skill would understand that this is merely applying the known autonomous route guidance to the abstract idea. Therefore, the 35 U.S.C. 101 rejection is maintained.
Applicant’s arguments, see pages 11 to 13, with respect to 35 U.S.C. 103 rejection have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Chai et al. (US 20170225583 A1) discloses [0026] “Referring to FIG. 4, the vehicle 100 can be brought into proximity of the charging station 200, either through the efforts of the driver of the vehicle 100 or by the vehicle self-driving system 620”;
LITJEN (US 20150077239 A1) discloses [0022] “FIGS. 1 and 2 are perspective and plan views, respectively, of one embodiment of an EV charger 100 that has distance and near displays (105, 110) for providing information to actual users, potential users, or service technicians of the EV charger that may positioned at remote or proximal positions from the EV charger. A person that is proximal to the user may be an actual user and so may need information related to the operation of the charger or status of the vehicle such as connecting or disconnecting the charger to the vehicle, starting or stopping the charging, authorizing the charging, payment, specific charging rate, SOC, cost of charging (e.g., dollars and cents) error messages, maps, interactive screens and menus, and the like”;
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HENRY H JUNG whose telephone number is (571)270-5018. The examiner can normally be reached Mon - Fri 9:30 - 5:30.
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/HENRY H JUNG/ Examiner, Art Unit 3695
/CHRISTINE M Tran/ Supervisory Patent Examiner, Art Unit 3695
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