CTNF 19/034,249 CTNF 79297 Von Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The information disclosure statement (IDS) submitted on January 22, 2025, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Objections 07-29-01 AIA Claim 15 objected to because of the following informalities: This method claim currently depends from claim 1, a system claim. For examination purposes, Examiner understands the claim should depend from claim 14 . Appropriate correction is required. Claim Rejections - 35 USC § 101 07-04-01 AIA 07-04 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. Claim(s) 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is not eligible for patenting. There are two criteria for determining subject matter eligibility: (a) first, a claimed invention must fall within one of the four statutory categories of invention set forth in 35 U.S.C. 101, i.e., process, machine, manufacture, or composition of matter ( Step 1 ); and (b) second, a claimed invention must be directed to patent-eligible subject matter and not a judicial exception (unless the claim as a whole includes additional limitations amounting to significantly more than the exception) ( Step 2 ). Step 1: Claim(s) 1-20 is/are within the four potentially eligible categories of invention (a process, a machine and an article of manufacture, respectively), satisfying Step 1 of the Subject Matter Eligibility (SME) test. Step 2: As per Prong One of Step 2A of the §101 eligibility analysis set forth in MPEP 2106, the Examiner notes that the claims recite mental processes. More specifically, independent claim 1 recites: a neural network processor configured to predict a charging call with respect to the electric vehicle through a machine learning (ML) model trained based on a dataset generated by preprocessing past charging call data; and a deployment optimizer configured to optimize deployment of the charging device based on the predicted charging call. Independent claim 14 recites: predicting , by a processor, a charging call with respect to the electric vehicle through a machine learning (ML) model trained based on a dataset generated by preprocessing past charging call data; and optimizing , by the processor , deployment of the charging device based on the predicted charging call. The predicting and optimizing of claims 1 and 14 are observations/evaluations that can practically be performed in the mind or with pen and paper as such, they are mental processes. Further, the predicting and optimizing are considered certain method of organizing human activity as they relate to commercial interactions. Independent claim 20 recites: generating a prediction heatmap of charging calls during a prediction period using a first ML model trained through supervised learning based on a dataset about past charging call data; and clustering the prediction heatmap through unsupervised learning using a second ML model. The generating and clustering steps of claim 20 are also observations and evaluations that can be practically performed in the mind or with pen and paper and are considered mental process. The steps are also certain methods of organizing human activity as they relate to commercial interactions. The nominal recitation of a system comprising a neural network processor that uses machine learning and a deployment optimizer in claim 1; the processor and using a machine learning model in claim 10; and the processor and memory and using machine learning in claim 20 does not necessarily preclude the claim from reciting an abstract idea as evidenced by the analysis at Prong 2 of Step 2A. Regarding Prong Two of Step 2A , a claim reciting an abstract idea must be analyzed to determine whether any additional elements in the claim integrate the judicial exception into a practical application. Limitations that are indicative of integration into a practical application include: Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a); Applying or using a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo; Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b); Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c); and Applying or using 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, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018. In this case, the independent claims do not include limitations that meet the criteria listed above, thus the abstract idea is not integrated into a practical application. Independent claim 1 recites a system comprising a neural network processor that uses machine learning and a deployment optimizer. Independent claim 10 recites a processor and using a machine learning model. Independent claim 20 recites a processor and memory and using machine learning. In each claim, the additional elements amounts to using a computer as a tool to implement a mathematical model and to perform the abstract idea and does not integrate the abstract idea into a practical application. The dependent claims further limit the abstract idea and some recite additional elements that do not integrate the abstract idea into a practical application. Dependent claims 2-4 recite details of generating a spatiotemporal dataset, removing outliers and generating a heatmap. As in claim 1, these are mental process evaluations and also certain method of organizing human activity is it relates to commercial interaction. The system amounts to using a computer as a tool to perform the abstract idea. There is no integration into a practical application. Dependent claims 5-11 recite details of the generating a charging call prediction and heat map generating. These are mental process evaluations and also certain method of organizing human activity is it relates to commercial interaction. The system amounts to using a computer as a tool to perform the abstract idea. The machine learning and neural network are mathematical algorithms implemented by a computer. There is no integration into a practical application. Dependent claims 12-13 recite details of the charging service and optimizing deployment of charging device. These are mental process evaluations and also certain method of organizing human activity is it relates to commercial interaction. The system amounts to using a computer as a tool to perform the abstract idea. There is no integration into a practical application. The claims do not include limitations beyond generally linking the use of the abstract idea to a particular technological environment. When considered individually and in combination, the system/software claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. The invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense. Lastly and in accordance with Step 2B , 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, and when considered individually and in combination, the additional elements amount to no more than mere instruction to apply the exception using generic computer component. Mere instruction to apply an exception using generic computer components cannot provide an inventive concept. 07-30-03-h AIA CLAIM INTERPRETATION 07-30-03 AIA The following is a quotation of 35 U.S.C. 112(f): (f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph: An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof. 07-30-05 The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked. As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph: (A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function; (B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and (C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function. Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function. Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function. Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a deployment optimizer” in claim 1 and “a feature engineering device” in claim 2. Examiner is interpreting the hardware structure of the claimed “optimizer” as being hardware processors within a computer, as recited in paragraph [0057] of the specification. Examiner is interpreting the corresponding algorithms for the deployment optimizer as recited in paragraphs [0088-0091] of the specification. The hardware structure of the claimed “device” is interpreted as hardware processors within a computer, as recited in paragraph [0057] of the specification. Examiner interprets the corresponding algorithms for the device as recited in paragraphs [0059-0065]. Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim (s) 1-4, 12-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al , “Providing Active Charging Services: An Assignment Strategy With Profit-Maximizing Heat Maps for Idle Mobile Charging Stations” , in view of Miyazaki et al, “Outlier Removal for Improving the Accuracy of Electric Vehicle Behavior Prediction” . As per claim 1, Liu et al discloses a system for managing a charging device for an electric vehicle, the system comprising: a processor configured to predict a charging call with respect to the electric vehicle by preprocessing past charging call data (p2140, Case B – tracks electric vehicles to determine potential charging in future; p2145, stage B – uses predicted charging demand based on past charging); and a deployment optimizer configured to optimize deployment of the charging device based on the predicted charging call (p2140 – concept of heatmaps is used to depict potential charge demand of electric vehicle to assist in optimize the assignment of mobile charging stations (MCS). While Liu et al discloses using predicted or potential charging demand it does not explicitly disclose predicting the demand through a machine learning (ML) model trained based on a dataset generated. Miyazaki et al discloses a machine learning model to predict state of charge for electric vehicles, the model trained with data excluding noise (p29-30). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Liu et al the ability to generate the charging demand as taught by the machine learning model in Miyazaki et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. As per claim 2, Liu et al discloses the system of claim 1,further including a feature engineering device configured to generate a spatiotemporal dataset for the charging call (page 2145 – use of position and time data representing charging need to generate heat maps). Liu et al does not explicitly disclose removing outliers from the past charging call data. Miyazaki et al discloses outlier detection techniques to remove irregular charging status behaviors (p29). It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Liu et al to generate the data set using outlier detection and removal as a model trained with data excluding noise has more robust prediction and improved accuracy (Miyazaki, p29). As per claim 3, Liu et al discloses the system of claim 2 further configured to generate a heatmap of call history by mapping the past charging call data to service areas with respect to a charging service for the electric vehicle (p2140 – concept of heatmaps is used to depict potential charge demand of electric vehicle to assist in optimize the assignment of mobile charging stations (MCS). As per claim 4, Liu et al discloses the system of claim 3, wherein for the generating of the heatmap of the call history by mapping the past charging call data to the service areas with respect to the charging service for the electric vehicle, the feature engineering device is further configured to collect the heatmap of the call history during a time window having a predetermined time size (p2142 – heat maps represent data over time; p2145 – data is uploaded charging demand during a timeslot to generate heatmap). As per claim 12, Liu et al discloses the system of claim 1, wherein the charging device is included in a charging vehicle that provides a mobile charging service for the electric vehicle (Abstract – mobile charging stations deployed to complement fixed charging stations). As per claim 13, Liu et al discloses the system of claim 12, wherein for the optimizing of the deployment of the charging device based on the predicted charging call, the deployment optimizer is further configured to provide a prediction heatmap of the predicted charging call to the charging vehicle including the charging device or a provider of the mobile charging service (p2140 – concept of heatmaps is used to depict potential charge demand of electric vehicle to assist in optimize the assignment of mobile charging stations (MCS). Claims 14-16 are directed to the method performed by the system of claims 1 and 3 and the limitations are substantially the same therefore the rejections applied to claims 1 and 3 also apply to claims 14-16 . 07-21-aia AIA Claim (s) 5-7, 9, 17, 18 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al , “Providing Active Charging Services: An Assignment Strategy With Profit-Maximizing Heat Maps for Idle Mobile Charging Stations” , in view of Miyazaki et al, “Outlier Removal for Improving the Accuracy of Electric Vehicle Behavior Prediction” , and Choi et al, US 2021/0300198 . As per claim 5, the combination of Liu et al and Miyazaki et al fails to explicitly disclose wherein the neural network processor is further configured to infer a charging call for a prediction period using the first ML model trained based on the dataset through supervised learning. Choi et al discloses a neural network charging learning model which uses charging data for supervised learning to train the neural network (0055, 0077-0080). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Liu et al and Miyazaki et al the ability to use supervised learning to train the neural network as taught by Choi et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. As per claim 6, Liu et al discloses generate and process a spatiotemporal dataset for the charging call to output heat map generation (page 2145 – use of position and time data representing charging need to generate heat maps) but fails to disclose, while Miyazaki et al discloses a machine learning model to process the dataset (p29-30 - predict state of charge for electric vehicles, the model trained with data excluding noise). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Liu et al the ability to generate the charging demand as taught by the machine learning model in Miyazaki et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Claims 17 and 18 are directed to the method performed by the system of claims 5 and 6 and the limitations are substantially the same therefore the rejections applied to claims 5 and 6 also apply to claims 17 and 18. As per claim 7, Liu et al discloses wherein the inference result image output is a prediction heatmap of counts of future charging calls, but fails to explicitly disclose the heatmaps are generated from the first ML model. Miyazaki et al discloses a machine learning model to predict state of charge for electric vehicles, the model trained with data excluding noise and generating heatmaps for the data (p29-31). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Liu et al the ability to generate the charging demand as taught by the machine learning model in Miyazaki et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. As per claim 9, Liu et al fails to disclose however, Miyazaki et al discloses the system wherein the machine learning (ML) model includes a second ML model, and wherein the neural network processor is further configured to cluster the prediction heatmap through unsupervised learning using the second ML model (pg30, section II). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Liu et al the ability to use unsupervised learning to cluster the heatmap as taught by Miyazaki et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. As per claim 20, Liu et al discloses generating a prediction heatmap of charging calls during a prediction period based on a dataset about past charging call data (p2140 – concept of heatmaps is used to depict potential charge demand of electric vehicle to assist in optimize the assignment of mobile charging stations (MCS) but fails to explicitly disclose supervised learning to train the model. Choi et al discloses a neural network charging learning model which uses charging data for supervised learning to train the neural network (0055, 0077-0080). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Liu et al and Miyazaki et al the ability to use supervised learning to train the neural network as taught by Choi et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Liu et al further fails to explicitly disclose the neural network processor is further configured to cluster the prediction heatmap through unsupervised learning using the second ML model. Miyazaki et al discloses the system wherein the machine learning (ML) model includes a second ML model, and wherein the neural network processor is further configured to cluster the prediction heatmap through unsupervised learning using the second ML model (pg30, section II). It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Liu et al the ability to use unsupervised learning to cluster the heatmap as taught by Miyazaki et al since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable . 07-21-aia AIA Claim (s) 8 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al , “Providing Active Charging Services: An Assignment Strategy With Profit-Maximizing Heat Maps for Idle Mobile Charging Stations” , in view of Miyazaki et al, “Outlier Removal for Improving the Accuracy of Electric Vehicle Behavior Prediction” , Choi et al , US 2021/0300198, and Farrell, US 10541051 . As per claim 8, Liu et al discloses heatmaps presenting data for different time intervals (p2147-2148) but fails to explicitly disclose a time interval of a next half-year, a next quarter or a next month. Farrell discloses battery monitoring wherein heatmaps are used to predict over time intervals including 6-month prediction, for example. It would have been obvious to one of ordinary skill in the art at the time of the invention to include in the system of Liu et al the ability to project charge need over a time period such as 6-months as taught by Farrell since the claimed invention is merely a combination of old elements and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Claim 19 is directed to the method performed by the system of claim 8 and the limitations are substantially the same therefore the rejections applied to claim 8 also applies to claim 19 . 07-21-aia AIA Claim (s) 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al , “Providing Active Charging Services: An Assignment Strategy With Profit-Maximizing Heat Maps for Idle Mobile Charging Stations” , in view of Miyazaki et al, “Outlier Removal for Improving the Accuracy of Electric Vehicle Behavior Prediction” , and Choi et al, US 2021/0300198 and Flynn et al, “ Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging” . As per claim 10, the combination of Liu et al and Miyazaki et al discloses wherein for the clustering the prediction heatmap through the unsupervised learning using the second ML model, the neural network processor is further configured to extract features from a clustered prediction heatmap based on population density (Liu, p2142, section C) but fails to explicitly disclose using at least one convolution layer based on population density of service areas with respect to a charging service for the electric vehicle. Flynn et al disclose processing an EV charging heatmap through a convolutional neural network to explore population density (p145-156). As per claim 11, Liu et al discloses the system of claim 10, wherein for the clustering of the prediction heatmap through the unsupervised learning using the second ML model, the neural network processor is further configured to adjust the prediction heatmap based on status of chargers within the service areas (p2145 – heatmap is adjusted based on availability of charger based on tracking positions) . Conclusion 07-96 AIA The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Pertinent art is listed in the attached PTO-892 . Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHNNA LOFTIS whose telephone number is (571)272-6736. The examiner can normally be reached M-F 7:00am-3:30pm. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. 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JOHNNA LOFTIS Primary Examiner Art Unit 3625 /JOHNNA R LOFTIS/Primary Examiner, Art Unit 3625 Application/Control Number: 19/034,249 Page 2 Art Unit: 3625 Application/Control Number: 19/034,249 Page 3 Art Unit: 3625 Application/Control Number: 19/034,249 Page 4 Art Unit: 3625 Application/Control Number: 19/034,249 Page 5 Art Unit: 3625 Application/Control Number: 19/034,249 Page 6 Art Unit: 3625 Application/Control Number: 19/034,249 Page 7 Art Unit: 3625 Application/Control Number: 19/034,249 Page 8 Art Unit: 3625 Application/Control Number: 19/034,249 Page 9 Art Unit: 3625 Application/Control Number: 19/034,249 Page 10 Art Unit: 3625 Application/Control Number: 19/034,249 Page 11 Art Unit: 3625 Application/Control Number: 19/034,249 Page 12 Art Unit: 3625 Application/Control Number: 19/034,249 Page 13 Art Unit: 3625 Application/Control Number: 19/034,249 Page 14 Art Unit: 3625 Application/Control Number: 19/034,249 Page 15 Art Unit: 3625 Application/Control Number: 19/034,249 Page 16 Art Unit: 3625 Application/Control Number: 19/034,249 Page 17 Art Unit: 3625
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