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 the Claims
This Final action is in response to the applicant’s amendment/response of April 2, 2026.
Claims 4, 9, and 14 have been canceled.
Claims 1-3, 5-8, 10-13, and 15-19 are pending and have been considered as follows.
Response to Arguments
Applicant’s arguments/amendments with respect to the objection to the claims have been fully considered and are persuasive. Therefore, the objection to the claims as presented in the Office Action of February 20, 2026 has been withdrawn. However, new objection to the claims is presented below based on the amendments to the claims presented in the Amendment of 2 April 2026.
Applicant’s arguments/amendments with respect to the rejection of claims under 35 USC §112(b) have been fully considered and are partially persuasive. With respect to the rejection of claims 7, 8, 10, 17, and 18 under 35 USC §112(b), limitations within the claims have been amended and amendments overcome the rejection under 35 USC §112(b). However, the amendments to claims 1 and 11 do not overcome the rejection under 35 USC §112(b) going to the specific limitations of “the vehicle”. Therefore, the rejection of such claims under 35 USC §112(b) is maintained herein, and new rejection under 35 USC §112(b) is presented below based on the amendments to the claims presented in the Amendment of 2 April 2026.
Applicant’s arguments/amendments with respect to the rejection of claims under 35 USC § 101 have been fully considered and are not persuasive.
Specifically, applicant argues:
101- Step 2A
Per the Examiner, the additional elements do not integrate the abstract idea into a practical application. The applicant disagrees and the now claimed subject matter provides for greater accuracy, efficiency, and/or expedient results when predicting charging location(s). Instant Application at Paragraph [0071].
101- Step 2B
The Examiner has also noted the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Specifically, the Examiner has stated the claims only recite well-understood, routine, conventional (WURC) activity. However, by way of this amendment, the capture and use of image data of parking spot(s) to generate prediction data is now claimed. This is a practical application and serves as more than mere instructions for generic computer components.
In BASCOM GLOBAL INTERNET SERVICES V. AT&T MOBILITY it was clarified that "the non-conventional and non-generic arrangement of known, conventional pieces" may be considered significantly more than a judicial exception. BASCOM GLOBAL INTERNET SERVICES V. AT&T MOBILITY 827 F.3d 1341 (2016) at page 15. The novelty of the claimed subject matter is not in dispute and the now more limited claim scope discussed below cannot be said to be conventional nor generic (even if it contains some conventional pieces), thus satisfying the need for significantly more than the judicial exception.
The Examiner’s Response
The Examiner has carefully considered applicant’s arguments and respectfully disagrees. Applicant asserts that “101- Step 2A Per the Examiner, the additional elements do not integrate the abstract idea into a practical application. The applicant disagrees and the now claimed subject matter provides for greater accuracy, efficiency, and/or expedient results when predicting charging location(s). Instant Application at Paragraph [0071].” However, the Examiner respectfully disagrees. The Examiner submits that under its broadest reasonable interpretation, the amended limitations “generate/generating a vehicle type prediction associated with the at least one vehicle …” and “generate/generating an electrical vehicle charge point location prediction as a probability score …”, can reasonably be performed by a human mentally or with aid of pen and paper. Although, the claim recites the amended additional limitations of “collect/collecting real-time driving data …”, “retrieve/retrieving historical driving data …”, and “retrieve/retrieving point of interest (POI) data …”, the additional details do not recite additional elements that integrate the judicial exception into a practical application. The collecting and retrieving steps do not elevate this limitation from insignificant extra-solution data gathering. The claims as a whole merely describe how to generally “apply” the otherwise mental judgments in a generic or general purpose computing environment.
Further, Applicant asserts that “101- Step 2B The Examiner has also noted the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Specifically, the Examiner has stated the claims only recite well-understood, routine, conventional (WURC) activity. However, by way of this amendment, the capture and use of image data of parking spot(s) to generate prediction data is now claimed. This is a practical application and serves as more than mere instructions for generic computer components. In BASCOM GLOBAL INTERNET SERVICES V. AT&T MOBILITY it was clarified that "the non-conventional and non-generic arrangement of known, conventional pieces" may be considered significantly more than a judicial exception. BASCOM GLOBAL INTERNET SERVICES V. AT&T MOBILITY 827 F.3d 1341 (2016) at page 15. The novelty of the claimed subject matter is not in dispute and the now more limited claim scope discussed below cannot be said to be conventional nor generic (even if it contains some conventional pieces), thus satisfying the need for significantly more than the judicial exception.” However, the Examiner respectfully disagrees. The Examiner submits that the claim(s) does/do not recite any specific limitation or combination of limitations that are not well-understood, routine, conventional (WURC) activity in the field. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements in the claims amount to no more than insignificant extra-solution activity. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere performance of an action is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Therefore, the rejection of such claims under 35 USC § 101 rejection is maintained herein.
Examiner notes that the rejection has been modified reflecting the amendments most recently submitted by applicant.
Applicant’s arguments/amendments with respect to the rejection of claims under 35 USC § 103 have been fully 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.
Claim Objections
Claim 1 is objected to because of the following informalities:
Claim 1, lines 9-10, “the one or more databases” should read “one or more databases”.
Claim 1, line 11, “one or more databases” should read “the one or more databases”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3, 5-8, 10-13, and 15-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As to claim 1, the limitations “a user of the vehicle” and “a navigation unit of the vehicle” at lines 5 are unclear. Specifically, it is unclear to the Examiner if this is the same “at least one vehicle” previously recited at line 4 or different vehicle. The Examiner notes that if it is a different vehicle, there is insufficient antecedent basis for this limitation in the claim. For purposes of examination, the Examiner is interpreting the limitation to be “the at least one vehicle”.
As to claim 11, the limitation “a user of the vehicle” at line 3 is unclear. Specifically, it is unclear to the Examiner if this is the same “at least one vehicle” previously recited at line 2 or different vehicle. The Examiner notes that if it is a different vehicle, there is insufficient antecedent basis for this limitation in the claim. For purposes of examination, the Examiner is interpreting the limitation to be “the at least one vehicle”.
Further, the limitations “retrieving, by a processor, point of interest (POI) data …”, “generating, by a processor, a vehicle type prediction …”, and “generating, by a processor, an electrical vehicle charge point location prediction ….” at lines 9, 11, and 15 are unclear. Specifically, it is unclear to the Examiner if this is the same “a processor” previously recited at line 7 or different. For purposes of examination, the Examiner is interpreting the limitations to be “the processor”.
Furthermore, the limitation “retrieving, by a processor, point of interest (POI) data from one or more databases” at lines 9-10 is unclear. Specifically, it is unclear to the Examiner if this is the same “one or more databases” previously recited at line 8 or different. For purposes of examination, the Examiner is interpreting the limitation to be “the one or more databases”.
Claims 2, 3, 5-8, 10, 12, 13, and 15-19 are rejected as being dependent upon a rejected claim.
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, 5-8, 10-13, and 15-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In January, 2019 (updated October 2019), the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claims 1 and 11 are directed toward non-statutory subject matter, as shown below:
STEP 1: Do claims 1 and 11 fall within one of the statutory categories? Yes. The claims are directed toward a machine and a process which falls within one of the statutory categories.
STEP 2A (PRONG 1): Are the claims directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claims are directed to an abstract idea.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
The independent claims (claims 1 and 11) recite the limitation of “generate/generating a vehicle type prediction associated with the at least one vehicle based on the collected real-time driving data, the retrieved historical driving data, and the POI data, wherein the generated vehicle type prediction is stored in the one or more databases” and “generate/generating an electrical vehicle charge point location prediction as a probability score based on at least the vehicle type prediction and the parking location information for the at least one vehicle”. Under its broadest reasonable interpretation, this limitation, as drafted, can reasonably be performed in the human mind or by a human using a pen and paper, otherwise considered a mental process, which is an abstract idea. For example, the claim limitations encompass a person looking at (observing) the data and determines a vehicle type prediction; and determines an electrical vehicle charge point location prediction as a probability score. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that “can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). As such, the claim encompasses a user (person) simply generating a vehicle type prediction associated with the at least one vehicle based on the collected real-time driving data, the retrieved historical driving data, and the POI data, wherein the generated vehicle type prediction is stored in the one or more databases; and generating an electrical vehicle charge point location prediction as a probability score based on at least the vehicle type prediction and the parking location information for the at least one vehicle in his/her mind or by a human using a pen and paper. The mere nominal recitation of an apparatus (claim 1), at least one processor/a processor (claims 1 and 11), or at least one memory (claim 1) does not take the claim limitation out of the mental processes grouping. Thus, the claim recites a mental process.
STEP 2A (PRONG 2): Do the claims recite additional elements that integrate the judicial exception into a practical application? No, the claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses 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 more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claims 1 and 11 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. This judicial exception is not integrated into a practical application because the claim(s) recites additional elements of “collect/collecting real-time driving data for at least one vehicle from a mobile device associated with a user of the vehicle or a navigation unit of the vehicle, wherein the collected real-time driving data includes at least parking location information for the at least one vehicle, wherein the collected parking location information includes image data of at least one parking spot”, “retrieve/retrieving historical driving data associated with the at least one vehicle from the one or more databases”, “retrieve/retrieving point of interest (POI) data from one or more databases”, an apparatus (claim 1), at least one processor/a processor (claims 1 and 11), and at least one memory (claim 1). The collecting and retrieving steps are recited at a high level of generality (i.e. as a general means of receiving/gathering data) and amount to no more than data gathering, which is a form of extra solution activity. Regarding the additional limitation(s) of “an apparatus” in claim 1, “at least one processor/a processor” in claims 1 and 11, and “at least one memory” in claim 1, the Examiner submits the limitations are merely tool(s) being used to perform the abstract idea (or instructions to implement the abstract idea on a computer). Further, the “an apparatus”, “at least one processor/a processor”, and “at least one memory” are recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer. The component(s) merely automate(s) the aforementioned step(s) and thus do/does not integrate a judicial exception into a “practical application”. See MPEP 2106.05(f). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of computers. It should be noted that because the courts have made it clear that mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of these computer components does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 224-26 (2014). Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to the abstract idea.
STEP 2B: Do the claims recite additional elements that amount to significantly more than the judicial exception? No, the claims do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
The claim(s) does/do not recite any specific limitation or combination of limitations that are not well-understood, routine, conventional (WURC) activity in the field. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “the apparatus”, “the at least one processor/the processor”, and “the at least one memory” amount to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the additional elements in the claims amount to no more than insignificant extra-solution activity. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere performance of an action is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
CONCLUSION
Thus, since claims 1 and 11 are: (a) directed toward an abstract idea, (b) does not recite additional elements that integrate the judicial exception into a practical application, and (c) does not recite additional elements that amount to significantly more than the judicial exception, it is clear that claims 1 and 11 are directed towards non-statutory subject matter.
Examiner additionally notes claims 2, 3, 5-8, and 10 depend from claim 1 and claims 12, 13, and 15-19 depend from claim 11.
Dependent claims 2, 3, 5-8, 10, 12, 13, and 15-19 further limit the abstract idea without integrating the abstract idea into practical application or adding significantly more. Each of the claimed limitations either expand upon or add either 1) new mental process, 2) a new additional element, 3) previously presented mental process, and/or 4) a previously presented additional element.
As such, claims 1-3, 5-8, 10-13, and 15-19 are rejected under 35 USC 101 as being drawn to an abstract idea without significantly more, and thus are ineligible.
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.
Claim(s) 1-3, 5, 7, 8, 10-13, 15, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kang et al., US 2025/0153597 A1, hereinafter referred to as Kang, in view of Sharifi, US 2024/0175696 A1, hereinafter referred to as Sharifi, in view of Salter et al., US 2023/0076816 A1, hereinafter referred to as Salter, and further in view of Campbell, US 2025/0124359 A1, hereinafter referred to as Campbell, respectively.
As to claim 1, Kang teaches an apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and computer program code configured to, with the at least one processor, cause the apparatus to at least (see at least paragraphs 111-114, Kang):
collect real-time driving data for at least one vehicle from a mobile device associated with a user of the vehicle or a navigation unit of the vehicle, wherein the collected real-time driving data includes at least parking location information for the at least one vehicle (see at least paragraphs 56 and 72-77 regarding collecting vehicle driving information (e.g., destination information, ignition on/off information, etc.) from the vehicles 201, 202, and 203. See also at least paragraphs 80-95 regarding receiving vehicle information (destination information, vehicle SOC information, a target charging amount, charging station preference information, charging station usage history information, etc.) of the target vehicle 204 to recommend a charging station from the data request processor 144, Kang);
retrieve historical driving data associated with the at least one vehicle from the one or more databases (see at least paragraphs 56 and 72-77 regarding collecting charging history information from the vehicles 201, 202, and 203. In the instant case, the vehicles 201, 202, and 203 may include all surrounding electric vehicles. See also at least paragraphs 80-95, Kang);
retrieve point of interest (POI) data from one or more databases (see at least paragraphs 47, 56, and 72-77. See also at least paragraphs 80-95 regarding receiving average stay time information for each POI from the data processor 142, Kang).
Kang does not explicitly teach generating a vehicle type prediction associated with the at least one vehicle based on the collected real-time driving data, the retrieved historical driving data, and the POI data, wherein the generated vehicle type prediction is stored in the one or more databases.
However, such matter is taught by Sharifi (see at least paragraphs 18-30 regarding determining a vehicle type associated with a particular vehicle and providing customized navigation information that accounts for the determined vehicle type. For example, a user may use a navigation application to access directions to a particular location and/or information about their location. In some examples, the specific vehicle type of a vehicle that a user is currently riding in or driving can be useful in determining the most useful navigation information that can be provided by the navigation application. To determine the specific vehicle type, the navigation application can (e.g., upon startup, periodically, and/or when a navigational query is submitted) access one or more vehicle type identification signals available to the navigation application. Based on these vehicle type identification signals, the navigation application can automatically determine a vehicle type associated with the current vehicle of the user. See also at least paragraphs 43-44 and 61 regarding in response to the navigation system 130 determining that the navigation services are being accessed (e.g., upon initiation of the navigation system 130, periodically while the navigation system 130 is running, and/or upon the user submitting a request or query to the navigation system 130), the signal analysis system 120 (e.g., in response to a request from the navigation system 130) can access one or more vehicle type identification signals that can be used to determine the vehicle type of vehicle for which navigation services are being accessed (e.g., the vehicle in which the computing device is physically located). The user computing device 100 can include one or more sensors 210 that can be used to determine information, with the express permission of the user, associated with the environment of the user computing device 100 or information associated with the user of the user computing device 100 (such as the position or movement of the user). In some examples, the sensors 210 can include a motion sensor to detect movement of the device or the associated user, a location sensor (e.g., a GPS) to determine the current location of the user computing device 100. See also at least paragraphs 67-69).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of Sharifi which teaches generating a vehicle type prediction associated with the at least one vehicle based on the collected real-time driving data, the retrieved historical driving data, and the POI data, wherein the generated vehicle type prediction is stored in the one or more databases with the system of Kang as both systems are directed to a system and method for identifying the vehicle-related information and providing the navigational information based on the identified information, and one of ordinary skill in the art would have recognized the established utility of generating a vehicle type prediction associated with the at least one vehicle based on the collected real-time driving data, the retrieved historical driving data, and the POI data, wherein the generated vehicle type prediction is stored in the one or more databases and would have predictably applied it to improve the system of Kang.
Kang, as modified by Sharifi, does not explicitly teach wherein the collected parking location information includes image data of at least one parking spot.
However, such matter is taught by Salter (see at least Abstract regarding obtaining information about a battery charging lot and evaluates the information to identify a battery charging station having a first charging cable that includes a first type of plug which is compatible for coupling to a charging port of the BEV. See also at least paragraphs 22-24 regarding capturing images of objects located in front of the BEV 125. The camera 140, which can be mounted on a rear bumper of the BEV 125, is arranged to capture images of objects located behind the BEV 125 and may also be arranged to capture images of painted markings such as, for example, lines defining a parking spot. The camera 165, which can be mounted on a front bumper of the BEV 125, is arranged to capture images of objects such as, for example, lines defining a parking spot).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of Salter which teaches wherein the collected parking location information includes image data of at least one parking spot with the system of Kang, as modified by Sharifi, as both systems are directed to a system and method for identifying the charging station based on the collected sensor data, and one of ordinary skill in the art would have recognized the established utility of having wherein the collected parking location information includes image data of at least one parking spot and would have predictably applied it to improve the system of Kang as modified by Sharifi.
Kang, as modified by Sharifi and Salter, does not explicitly teach generating an electrical vehicle charge point location prediction as a probability score based on at least the vehicle type prediction and the parking location information for the at least one vehicle.
However, such matter is taught by Campbell (see at least paragraphs 27-31 regarding variability in the properties and capabilities of charging infrastructure and the properties and requirements of EVs results in difficulty reliably predicting which particular charging infrastructure a driver of an EV may select. Aggregation of multiple data related to the EV—e.g., power consumption, power draw requirements, and distance to nearest charging infrastructure—may be assessed in view of multiple data related to known charging infrastructure—e.g., power output, charger type, and receptacle type—to dynamically predict a selection of particular charging infrastructure by the driver of the EV. … When performing peer-to-peer EV charging functions, it may be helpful for a system to suggest an EV charger of compatible type and electrical characteristics to select for immediate charging or for a scheduled charging in the future. To make predictions or suggestions, a probabilistic model (or other model as described below in conjunction with FIG. 14) can be used to make an inference (or prediction) about aspects of peer-to-peer EV charging such as a charger to schedule. See also at least paragraphs 59-63).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of Campbell which teaches generating an electrical vehicle charge point location prediction as a probability score based on at least the vehicle type prediction and the parking location information for the at least one vehicle with the system of Kang, as modified by Sharifi and Salter, as both systems are directed to a system and method for recommending a charging station for an electric vehicle based on vehicle information and charging station information, and one of ordinary skill in the art would have recognized the established utility of generating an electrical vehicle charge point location prediction as a probability score based on at least the vehicle type prediction and the parking location information for the at least one vehicle and would have predictably applied it to improve the system of Kang as modified by Sharifi and Salter.
As to claim 2, Kang teaches wherein the electrical vehicle charge point location prediction is based on the vehicle type prediction and the parking location information for at least two vehicles (see at least paragraphs 72-77 and 85-95, Kang).
As to claim 3, Kang teaches wherein the electrical vehicle charge point location prediction is based on aggregated vehicle type prediction and parking location information for vehicles in a parking lot or parking garage (see at least paragraphs 80-95 regarding in a case where the average stay time at the restaurant A is 100 minutes and the time required to perform fast charging by 60% of the above SOC is 30 minutes, the target vehicle 204 may be parked at a charging position for more than one hour even after charging is completed, a fine may be imposed, and in the instant case, a charging station that takes longer to charge is recommended, so a parking time after charging is completed is less than one hour, so the fine may be avoided. For example, a charging station that takes longer to charge may have a slower charging speed. The data analyzer 143 may be configured to select a charging station by determining a type of charger at the charging station, whether a charger capable of charging exists, and whether it is fast charging or slow charging. That is, the data analyzer 143 can be configured to select a charging station that matches a charger type of the target vehicle 204, and to select the charging station by determining a charging speed according to the charging time. For example, in a case where the target vehicle 204 stays at a POI for 30 minutes, the analyzer 143 may be configured to select a charging station which may charge at a slow speed in response to a case where a time it takes to slowly charge it to the target charging amount is 30 minutes, and a time it takes to rapidly charge it to the target charge amount is 10 minutes, Kang).
As to claim 5, Kang teaches wherein the collected parking location information includes frequency data for parking at a predesignated electric vehicle charge spot (see at least paragraphs 56 and 72-77 regarding collecting vehicle driving information (e.g., destination information, ignition on/off information, etc.) from the vehicles 201, 202, and 203. See also at least paragraphs 80-95 regarding receiving vehicle information (destination information, vehicle SOC information, a target charging amount, charging station preference information, charging station usage history information, etc.) of the target vehicle 204 to recommend a charging station from the data request processor 144. The charging station usage history information may be transmitted to the datarequest processor 144 in response to the target vehicle 204 enters a destination, and charging station preference information may be directly inputted by a user and transmitted to the data request processor 144. For example, in a case where the user of the target vehicle 204 used a charging station A 10 times and used a charging station B 5 times, the data analyzer 143 may be preferentially configured to recommend the charging station A, which is used more frequently, Kang).
As to claim 7, Kang teaches wherein the historical driving data includes vehicle driving range or historical parking information for the at least one vehicle (see at least paragraphs 70-77 and 80-95 regarding the charging station usage history information may be obtained by transmitting charging history information to the data collector 141 after the target vehicle 204 is fully charged each time the target vehicle 204 is charged, Kang).
As to claim 8, Kang teaches wherein the electrical vehicle charge point location prediction is utilized to control at least one of: a vehicle navigation system, a vehicle control system, a vehicle electronic control unit, or an autonomous vehicle control system associated with the at least one vehicle (see at least FIG. 2 and paragraphs 43-48, Kang).
As to claim 10, Kang, as modified by Sharifi and Salter, does not explicitly teach providing, as an input, the collected real-time driving data, the retrieved historical driving data, and the parking location information to a machine learning (ML) model; or receiving, as an output from the ML model, the electrical vehicle charge point location prediction for the at least one vehicle.
However, Campbell teaches providing, as an input, the collected real-time driving data, the retrieved historical driving data, and the parking location information to a machine learning (ML) model (see at least paragraphs 27-31. See also at least paragraphs 59-73); and receiving, as an output from the ML model, the electrical vehicle charge point location prediction for the at least one vehicle (see at least paragraphs 27-31 regarding variability in the properties and capabilities of charging infrastructure and the properties and requirements of EVs results in difficulty reliably predicting which particular charging infrastructure a driver of an EV may select. Aggregation of multiple data related to the EV—e.g., power consumption, power draw requirements, and distance to nearest charging infrastructure—may be assessed in view of multiple data related to known charging infrastructure—e.g., power output, charger type, and receptacle type—to dynamically predict a selection of particular charging infrastructure by the driver of the EV. … When performing peer-to-peer EV charging functions, it may be helpful for a system to suggest an EV charger of compatible type and electrical characteristics to select for immediate charging or for a scheduled charging in the future. To make predictions or suggestions, a probabilistic model (or other model as described below in conjunction with FIG. 14) can be used to make an inference (or prediction) about aspects of peer-to-peer EV charging such as a charger to schedule. See also at least paragraphs 59-73 regarding generating an output (e.g., a suggestion, a prediction, a classification, etc.)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of Campbell which teaches providing, as an input, the collected real-time driving data, the retrieved historical driving data, and the parking location information to a machine learning (ML) model; and receiving, as an output from the ML model, the electrical vehicle charge point location prediction for the at least one vehicle with the system of Kang, as modified by Sharifi and Salter, as both systems are directed to a system and method for recommending a charging station for an electric vehicle based on vehicle information and charging station information, and one of ordinary skill in the art would have recognized the established utility of providing, as an input, the collected real-time driving data, the retrieved historical driving data, and the parking location information to a machine learning (ML) model; and receiving, as an output from the ML model, the electrical vehicle charge point location prediction for the at least one vehicle and would have predictably applied it to improve the system of Kang as modified by Sharifi and Salter.
As to claim 11, Examiner notes claim 11 recites similar limitations to claim 1 and is rejected under the same rational.
As to claim 12, Examiner notes claim 12 recites similar limitations to claim 2 and is rejected under the same rational.
As to claim 13, Kang teaches wherein the electrical vehicle charge point location prediction is based on aggregated vehicle type prediction and parking location information for vehicles in a town, city, or neighborhood (see at least paragraphs 80-95 regarding an average time may be determined for stay times of the POIs of the vehicles 201, 202, and 203. For example, in a case where the vehicle 201 stays at a restaurant A for 50 minutes, the vehicle 202 stays at the restaurant A for 40 minutes, and the vehicle 203 stays at the restaurant A for 30 minutes, an average stay time at the restaurant A is 30 minutes. In a case where the average stay time at the restaurant A is 100 minutes and the time required to perform fast charging by 60% of the above SOC is 30 minutes, the target vehicle 204 may be parked at a charging position for more than one hour even after charging is completed, a fine may be imposed, and in the instant case, a charging station that takes longer to charge is recommended, so a parking time after charging is completed is less than one hour, so the fine may be avoided. For example, a charging station that takes longer to charge may have a slower charging speed. The data analyzer 143 may be configured to select a charging station by determining a type of charger at the charging station, whether a charger capable of charging exists, and whether it is fast charging or slow charging. That is, the data analyzer 143 can be configured to select a charging station that matches a charger type of the target vehicle 204, and to select the charging station by determining a charging speed according to the charging time. For example, in a case where the target vehicle 204 stays at a POI for 30 minutes, the analyzer 143 may be configured to select a charging station which may charge at a slow speed in response to a case where a time it takes to slowly charge it to the target charging amount is 30 minutes, and a time it takes to rapidly charge it to the target charge amount is 10 minutes, Kang).
As to claim 15, Examiner notes claim 15 recites similar limitations to claim 5 and is rejected under the same rational.
As to claim 17, Examiner notes claim 17 recites similar limitations to claim 7 and is rejected under the same rational.
As to claim 18, Examiner notes claim 18 recites similar limitations to claim 8 and is rejected under the same rational.
As to claim 19, Kang, as modified by Sharifi, does not explicitly teach wherein the electrical vehicle charge point location prediction is used to cede control of an autonomous vehicle control system.
However, such matter is taught by Salter (see at least Abstract regarding obtaining information about a battery charging lot and evaluates the information to identify a battery charging station having a first charging cable that includes a first type of plug which is compatible for coupling to a charging port of the BEV. See also at least paragraph 65 regarding the input/output interface 685 may be configured to support transfer of an advisory from the battery charging advisory system 150 to the vehicle computer 145. In one implementation, the advisory may enable the vehicle computer 145 to autonomously maneuver the BEV 125 into a parking position for charging the battery of the BEV 125).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of Salter which teaches wherein the electrical vehicle charge point location prediction is used to cede control of an autonomous vehicle control system with the system of Kang, as modified by Sharifi, as both systems are directed to a system and method for identifying the charging station based on the collected sensor data, and one of ordinary skill in the art would have recognized the established utility of having wherein the electrical vehicle charge point location prediction is used to cede control of an autonomous vehicle control system and would have predictably applied it to improve the system of Kang as modified by Sharifi.
Claim(s) 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kang et al., US 2025/0153597 A1, hereinafter referred to as Kang, in view of Sharifi, US 2024/0175696 A1, hereinafter referred to as Sharifi, in view of Salter et al., US 2023/0076816 A1, hereinafter referred to as Salter, in view of Campbell, US 2025/0124359 A1, hereinafter referred to as Campbell, and further in view of SAKAIDA et al., US 2019/0178661 A1, hereinafter referred to as SAKAIDA, respectively.
As to claim 6, Kang, as modified by Sharifi, Salter, and Campbell, does not explicitly teach wherein the collected parking location information includes frequency data for parking at a gasoline pump.
However, such matter is taught by SAKAIDA (see at least paragraphs 104 and 114 regarding the priority provision unit 16 determines gas station E closest to the destination from among gas stations of a brand agreeing with brand A (see FIG. 10) of gas stations that the user uses most frequently, the reaching probability for the gas stations being level 1, as a display object and provides priority “2” to the gas station E. Next, in step S87, the priority provision unit 16 determines gas station H closest to the destination, convenience store C (see FIG. 11) being attached to gas station H, convenience store C being most frequently used by the user from among from among convenience stores attached to gas stations determined as suggested candidates).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use the system of SAKAIDA which teaches wherein the collected parking location information includes frequency data for parking at a gasoline pump with the system of Kang, as modified by Sharifi, Salter, and Campbell, as both systems are directed to a system and method for identifying the energy station for the vehicle based on the collected driving information, and one of ordinary skill in the art would have recognized the established utility of having wherein the collected parking location information includes frequency data for parking at a gasoline pump and would have predictably applied it to improve the system of Kang as modified by Sharifi, Salter, and Campbell.
As to claim 16, Examiner notes claim 16 recites similar limitations to claim 6 and is rejected under the same rational.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Raguse (US 20250065768 A1) regarding a system for determining an intention of a vehicle operator to use the charging station in the relevant region.
THIS ACTION IS MADE FINAL. 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.
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/K.S.P./Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666
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