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 Claims
This action is in reply to the amendment filed on 11/28/2025.
Claims 5-9, 14-18, and 20 are still withdrawn.
Claims 1-2, 4-11, and 13-20 are currently pending and claims 1-2, 4, 10-11, 13, and 19 have been examined.
This action is made FINAL.
Response to Arguments
Applicant’s arguments, see pages 8-11, filed 11/28/2025, with respect to the 35 U.S.C. 101 rejections of claims 1-2, 4, 10-11, 13, and 19 have been fully considered but are not persuasive. The 35 U.S.C. 101 rejections of claims 1-2, 4, 10-11, 13, and 19 have been maintained.
On pages 8-10, Applicant argues that the claims integrate their abstract idea (commercial interactions) into a practical application at Step 2A Prong 2. Applicant argues that the claimed invention provides a most suitable charging station for the user “based on practical technical features” of a machine learning recommendation model and a map application. Applicant further argues that the vehicle owner preference, charging reservation information, and the other listed factors in the claims used to provide a most suitable charging station are “objective factors” and not “commercial factors”, and thus allegedly are additional elements. Applicant points to the 08/04/2025 Memo, particularly the “Improvements consideration” discussion, and argues that the claims recite a particular solution to a problem or a particular way to achieve a desired outcome instead of an idea of a solution or outcome.
Arguing in particular about the machine learning model and map application on pages 9-10, Applicant points to specification [0076] and the variety of possible models that are considered in the disclosure. Applicant further argues that the claimed training of the pre-trained machine learning recommendation model “based on the positive and negative sample” is “a specific technical training manner”. Regarding the map application, Applicant argues that the map application is “used to determine and update the charging reservation information”. In sum, Applicant argues that these features integrate the commercial interaction of the claims into a practical application by providing a particular solution to a problem. Examiner respectfully disagrees.
Regarding the various considerations Applicant argues are additional elements on page 9, Examiner notes that the claim is taking these abstract factors and promoting a particular reservation at a particular charging station based on the abstract factors. While the factors may be “objective” in the sense that number of charging piles is known, owner preference parameter is quantified, etc., factors estimating available reservation inventory (numbers/locations/states of charging piles in view of the preference parameter) and reservation commitments (estimated states and reservation information of the piles) are commercial factors and still part of the abstract idea of determining which charging reservation at which charging station to promote/advertise to the user of a vehicle. Regardless of whether the factors are labeled “commercial”, the factors are still pieces of data that are used in the commercial interaction of promoting a charging station/reservation to a user. Accordingly, the factors themselves are not additional elements and are part of the abstract idea.
Regarding Applicant’s arguments about the machine learning recommendation model, Examiner notes that Applicant states on Page 10 of Remarks, “In fact, the present application does not limit the ranking model to non-neutral network model, but any suitable model including both neutral network model and non-neutral network model” (emphasis added). By covering “any suitable model” in the present application and claims, the claimed invention does not provide “a particular solution to a problem or a particular way to achieve a desired outcome”. Instead the claimed invention is claiming the idea of “any suitable” pre-trained machine learning model being used to take the various factors discussed above as inputs and output a target charging station. Instead of a particular solution, the present claims are claiming the idea of some suitable model that can turn the recited inputs into the target charging station output, hence Examiner’s previous characterization of the presently-claimed pre-trained machine learning model being akin to a black box.
Regarding the training of the machine learning model that Applicant argues is specific on Page 10 of Remarks, claim 1 recites “training the pre-trained machine learning recommendation model based on the positive sample and the negative sample”, in which “positive sample” means an instance in which the user arrives at the target station and a “negative sample” is an instance in which the user does not arrive at the target station. By reciting that the training is based on the positive sample and the negative sample, the claimed invention is claiming the idea of improving the pre-trained model using the positive and negative sample and not claiming a particular solution to improving the model. Examiner further notes that parameters of the pre-trained model, which Applicant argues are improved on page 10, are not recited in the claims. Instead the recited parameters are the position evaluation parameter of the station and the owner preference parameter, which are inputs to the model. Therefore, instead of reciting a particular, technical way in which the machine learning model is trained, the claimed invention claims the idea of improving the model using positive and negative feedback.
Regarding the map application, the map application is recited in claim 1 as determining that a first vehicle has initiated a route navigation to a candidate charging station is a vehicle to be charged and reserved the candidate charging station and determining the charging reservation information of the vehicle. The map application is being used as a tool to receive information on a vehicle that is going to a candidate charging station, what time the navigating vehicle will arrive and reserve the candidate charging station, and reservation information for the vehicle at the candidate station. This gathered information is needed to perform the abstract idea of recommending a charging station for a user of another vehicle based on travel, queueing and charging times of other vehicles at candidate charging stations. Per MPEP 2106.05(f)(2), using computers as a tool in its ordinary capacity does not integrate a judicial exception into a practical application.
Accordingly, while Examiner agrees that “a particular solution to a problem or a particular way to achieve a desired outcome” satisfies the Improvements consideration as Applicant argues on Page 9 regarding the August 4th Memo, Examiner respectfully disagrees that the claimed invention claims such “a particular solution to a problem or a particular way to achieve a desired outcome”. Particularly, Applicant acknowledges on Page 10 of Remarks that the argued pre-trained machine learning recommendation model may be “any suitable model including both neutral network model and non-neutral network model”. With respect to the training, claim 1 recites that the positive and negative samples are used in the training, but claim 1 does not recite how the samples are used to train the model and how the model is being improved. Instead, the claim is reciting the idea of improving the model using positive and negative results without technical detail as to how such training is realized. Finally, the map application as claimed is receiving information on vehicles that are heading to candidate charging stations and what the reservations for those vehicle are. Considering the elements in an ordered combination, Examiner’s previous analysis of the additional elements of the claims at Step 2A Prong 2, namely that the additional elements are merely applying the exception using generic computing components and generally linking the exception to the field of machine learning, is maintained.
Next, on pages 10-11 Applicant argues that the claimed invention amounts to significantly more than the judicial exception at Step 2B because the claims are distinguished over the prior art of record. Examiner respectfully disagrees. MPEP 2106.05 I. states “the search for an inventive concept should not be confused with a novelty or non-obviousness determination. See Mayo, 566 U.S. at 91, 101 USPQ2d at 1973 (rejecting "the Government’s invitation to substitute §§ 102, 103, and 112 inquiries for the better established inquiry under § 101 "). As made clear by the courts, the "‘novelty’ of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter." Intellectual Ventures I v. Symantec Corp., 838 F.3d 1307, 1315, 120 USPQ2d 1353, 1358 (Fed. Cir. 2016) (quoting Diamond v. Diehr, 450 U.S. at 188–89, 209 USPQ at 9). See also Synopsys, Inc. v. Mentor Graphics Corp., 839 F.3d 1138, 1151, 120 USPQ2d 1473, 1483 (Fed. Cir. 2016) ("a claim for a new abstract idea is still an abstract idea. The search for a § 101 inventive concept is thus distinct from demonstrating § 102 novelty."). In addition, the search for an inventive concept is different from an obviousness analysis under 35 U.S.C. 103…Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101” (emphasis added). Therefore, Applicant’s argument that the claimed invention must be eligible at Step 2B because the claimed invention was found to be non-obvious over the prior art of record is not persuasive.
In contrast, the additional elements of the claimed invention amount to instructions to apply the judicial exception using generic computing components and generally linking the exception to the field of machine learning at Step 2A Prong 2 discussed above. Per MPEP 2106.05(f) and MPEP 2106.05(h), instructions to apply an exception and generally linking an exception to a field of use, respectively, do not amount to significantly more than the judicial exception at Step 2B. Accordingly, Applicant’s arguments are not persuasive, and the 35 U.S.C. 101 rejections have been maintained.
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 1-2, 4, 10-11, 13, and 19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite recommending a target charging station for a user of a target vehicle.
As an initial matter, claims 1-2 and 4 fall into at least the process category of statutory subject matter. Claims 10-11 and 13 fall into at least the machine category of statutory subject matter. Finally, claim 19 falls into at least the manufacture category of statutory subject matter. Therefore, all claims fall into at least one of the statutory categories. Eligibility analysis proceeds to Step 2A.
Claim 1 recites the concept of recommending a charging station to a target user based on existing charging station reservations which is a certain method of organizing human activity including commercial interactions. A method, comprising: determining a set of candidate charging stations for a target vehicle; determining, for each candidate charging station in the set of candidate charging stations, an estimated time of arrival of the target vehicle, and obtaining an estimated state of a charging pile of the candidate charging station at the estimated time of arrival, wherein the estimated state of the charging pile of the candidate charging station is based on a current state of the charging pile and charging reservation information, wherein the charging reservation information comprises: an estimated time of charging and estimated duration of charging of a vehicle to be charged that has reserved the candidate charging station, and wherein the charging reservation information is determined by determining, based on a detected route navigation having the candidate charging station as a destination of the route navigation, a first vehicle that initiates the route navigation as a vehicle to be charged that has reserved the candidate charging station, and determining the charging reservation information of the first vehicle; obtaining, for each candidate charging station in the set of candidate charging stations, a plurality of recommendation factors of the candidate charging station, wherein the plurality of recommendation factors comprise: a charging station position evaluation parameter, a vehicle owner preference parameter characterizing a preference of the vehicle owner of the target vehicle for different types of charging stations, and a total number of charging piles at the candidate charging station; determining a target charging station from the set of candidate charging stations according to the obtained estimated states of the charging piles of the candidate charging stations in the set of candidate charging stations at the estimated times of arrival of the target vehicle, and recommending the target charging station as a recommended target charging station for charging the target vehicle, wherein determining the target charging station from the set of candidate charging stations comprises: inputting for each candidate charging station in the set of candidate charging stations, a time cost of charging and the plurality of recommendation factors of the candidate charging station to a pre-trained recommendation model, wherein the time cost of charging is determined according to an estimated duration of traveling to the candidate charging station, an estimated duration of queuing of the target vehicle going to the candidate charging station for charging, and the estimated duration of charging of the target vehicle going to the candidate charging station for charging; and selecting, from the set of candidate charging stations, the target charging station based on an output of the pre-trained recommendation model; based on the vehicle owner arriving at the target charging station, marking the recommended target charging station as a positive sample; based on the vehicle owner not arriving at the target charging station, marking the recommended target charging station as a negative sample all, as a whole, fall under the category of commercial interactions. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a processor, an electronic device, and a target map application. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. The additional elements of a pre-trained machine learning recommendation model and training the pre-trained machine learning recommendation model based on the positive sample and the negative sample amount to no more than generally linking the judicial exception to the field of machine learning. 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 combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components and generally link the judicial exception to the field of machine learning. Accordingly, even in combination, 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 claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a processor, an electronic device, and a target map application amounts to no more than mere instructions to apply the exception using generic computer components. Also as discussed above, the additional elements of a pre-trained machine learning recommendation model and training the pre-trained machine learning recommendation model based on the positive sample and the negative sample amount to no more than generally linking the judicial exception to the field of machine learning. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components and generally link the judicial exception to the field of machine learning. Mere instructions to apply an exception using generic computer components and generally linking the judicial exception to a field of use cannot provide an inventive concept. The claim is not patent eligible.
Claims 2 and 4 further limit the abstract idea of claim 1 without adding any new additional elements. Therefore, by the analysis of claim 1 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim 10 recites the concept of recommending a charging station to a target user based on existing charging station reservations which is a certain method of organizing human activity including commercial interactions. Instructions to perform the method according to claim 1 all, as a whole, fall under the category of commercial interactions. See details of claim 1 method above. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of an electronic device, at least one processor, a memory in communication with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and a target map application. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. The additional elements of a pre-trained machine learning recommendation model and training the pre-trained machine learning recommendation model based on the positive sample and the negative sample amount to no more than generally linking the judicial exception to the field of machine learning. 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 combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components and generally link the judicial exception to the field of machine learning. Accordingly, even in combination, 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 claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of an electronic device, at least one processor, a memory in communication with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and a target map application amount to no more than mere instructions to apply the exception using generic computer components. Also as discussed above, the additional elements of a pre-trained machine learning recommendation model and training the pre-trained machine learning recommendation model based on the positive sample and the negative sample amount to no more than generally linking the judicial exception to the field of machine learning. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components and generally link the judicial exception to the field of machine learning. Mere instructions to apply an exception using generic computer components and generally linking the judicial exception to a field of use cannot provide an inventive concept. The claim is not patent eligible.
Claims 11 and 13 further limit the abstract idea of claim 10 without adding any new additional elements. Therefore, by the analysis of claim 10 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim 19 recites the concept of recommending a charging station to a target user based on existing charging station reservations which is a certain method of organizing human activity including commercial interactions. Instructions to perform the method according to claim 1 all, as a whole, fall under the category of commercial interactions. See details of claim 1 method above. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a non-transitory computer-readable storage medium storing computer instructions, one or more processors, a computer, and a target map application. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. The additional elements of a pre-trained machine learning recommendation model and training the pre-trained machine learning recommendation model based on the positive sample and the negative sample amount to no more than generally linking the judicial exception to the field of machine learning. 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 combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components and generally link the judicial exception to the field of machine learning. Accordingly, even in combination, 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 claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a non-transitory computer-readable storage medium storing computer instructions, one or more processors, a computer, and a target map application amount to no more than mere instructions to apply the exception using generic computer components. Also as discussed above, the additional elements of a pre-trained machine learning recommendation model and training the pre-trained machine learning recommendation model based on the positive sample and the negative sample amount to no more than generally linking the judicial exception to the field of machine learning. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components and generally link the judicial exception to the field of machine learning. Mere instructions to apply an exception using generic computer components and generally linking the judicial exception to a field of use cannot provide an inventive concept. The claim is not patent eligible.
Novel/Non-Obvious
Regarding claim 1, the combination of Amano et al. (U.S. Pre-Grant Publication No. 2015/0286965, hereafter known as Amano) in view of Li et al. (Chinese Publication No. 112116125, hereafter known as Li), Wittl (U.S. Pre-Grant Publication No. 2019/0210468, hereafter known as Wittl), and Hu et al. (U.S. Pre-Grant Publication No. 2020/0294078, hereafter known as Hu) teaches the majority of the limitations of claim 1 as discussed in greater detail on pages 14-25 in the 04/03/2025 Office Action. Hu further teaches vehicle owner preference parameter characterizing a preference of the vehicle owner of the target vehicle for different types of charging stations (see [0109]-[0110] and [0113]-[0115], particularly [0110] “if the user prefers the price discount of a charging station, the user may increase the first preset weight value. The users' preference may be pre-analyzed based on historical data relating to charging stations the user selects or through questionnaires (or other forms” and [0115] “different charging stations may have different characteristics including the number of charging piles, types, parking fees, charging fee, etc. Therefore, the preset recommendation value may be determined based on the recommendation parameters (e.g., the number of the charging piles, the real-time parking fee, the charging unit price, etc.) and the corresponding preset percentages”). While Li teaches a neural network being trained and used to determine a charging station as discussed in the 04/03/2025 Office Action, the combination of Amano, Li, Wittl, and Hu does not explicitly teach marking target stations that the user arrives at as positive samples and target stations that the user does not arrive at as negative samples and training the machine learning model based on the positive and negative samples.
Kazi et al. (U.S. Pre-Grant Publication No. 2022/0172040, hereafter known as Kazi) teaches training a machine learning model based on positive and negative samples of users following or not following output recommendations in [0015] (“The user then selects one of the query suggestions. The electronic search mechanism records the original search query, the query suggestion that the user selected, and the query suggestion(s) that was/were not selected by the user. Thus, the electronic search mechanism tags (1) the original search query as the source search query, (2) the selected (e.g., clicked) query suggestion as a positive query suggestion, and (3) each non-selected query suggestion as a negative query suggestion. Such a set of three queries is referred to as a “sequence triple.” The positive and negative query suggestions are considered user feedback. All generated sequence triples are considered user feedback data and may be used to train a machine-learned model”). However, it would not have been obvious to one of ordinary skill in the art to combine Amano, Li, Wittl, Hu, and Kazi to obtain Applicant’s claimed invention.
Other prior art of record also fails to remedy the deficiency of the combination of Amano, Li, Wittl, and Hu. Telpaz et al. (U.S. Pre-Grant Publication No. 2022/0318859, hereafter known as Telpaz) teaches a machine learning model identifying user preferences based on the charging stations selected by users, but Telpaz does not explicitly teach using negative samples of stations not selected being incorporated into the training. Meroux et al. (U.S. Pre-Grant Publication No. 2024/0067031, hereafter known as Meroux) teaches using followed recommendations for battery charging as ground truth data to train an ML model, but likewise does not explicitly teach using negative samples of stations not selected being incorporated into the training. Keskin et al. (“Electric Vehicle Routing Problem with Time Windows and Stochastic Waiting Times at Recharging Stations”, presented at the 2019 Winter Simulation Conference 12/8/2019-12/11/2019, hereafter known as Keskin) teaches optimizing vehicle charging stations while considering queueing time at the stations, but likewise does not explicitly teach training a machine learning model using positive and negative samples.
Therefore, claim 1 is distinguished over the art of record. Claims 10 and 19 are distinguished over the art of record for similar reasoning to that discussed above. Dependent claims 2, 4, 11, and 13 are distinguished over the art of record by virtue of their respective independent claims.
Conclusion
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Friedman et al. (U.S. Pre-Grant Publication No. 2019/0311241) teaches a machine learning virtual assistant that recommends a nearby charging station to a vehicle user
Oh et al. (U.S. Pre-Grant Publication No. 2017/0276503) teaches recommending vehicle charging station with the shortest charge waiting time
Tate, Jr. et al. (U.S. Pre-Grant Publication No. 2012/0233077) teaches reserving a charging station that is expected to be available at a vehicle arrival time
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL C MORONEY whose telephone number is (571)272-4403. The examiner can normally be reached Mon-Fri 8:30-5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Resha H. Desai can be reached on (571) 270-7792. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/M.C.M./Examiner, Art Unit 3628
/RESHA DESAI/ Supervisory Patent Examiner, Art Unit 3628
Prosecution Insights