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 .
The current application is continuation-in-part of application No. 18/073189 filed on Dec. 1, 2022, now Pat. No. 11775872.
Response to Arguments
Applicant's arguments filed 10/07/2025 have been fully considered but are not persuasive to overcome the rejection. Examiner responds to the Applicant’s argument as the following reasons:
Regarding the claimed amendment: Applicant amended the independent claims with “recommending at least one change to the existing utility infrastructure based on the utility map” that is not described in the current application’s specification. The amendment raises a new matter, see the 112(a) rejection as shown below.
Regarding the claimed Rejections under 101: At page 8 through page 12, Applicant argues that the amendment of the independent claims 1 & 15 recites a specific improvement to the relevant technical field and thereby integrates the alleged abstract idea into a practical application. Examiner disagrees because the claimed invention providing method steps of “providing one or more vehicle parameter …; “iteratively training the ML model …; receiving …a target area …; providing the target area …; obtaining one or more target …”; and “determining …” in claims 1 & 15 that amount to mere data gathering. The steps of projecting, via the trained ML model, a utility load demand at target area; “generating a utility map …”; and recommending at least one change to the existing utility …” which amounts to mere post solution displaying, which is a form of insignificant extra-solution activity.
Therefore, the claims are ineligibility 101.
Regarding the claimed Rejections under 103: At page 15, Applicant argues that Dai and Sun fail to teach “generating a utility map illustrating capabilities of an existing utility infrastructure within the target area relative to the projected utility load demand; and recommending at least one change to the existing utility infrastructure based on the utility map.
Examiner disagrees the Applicant’s argument. Dai and Sun disclose the claimed subject matter which updated as shown below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-28 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding independent claims 1 & 15, recites “recommending at least one charge to the existing utility infrastructure based on the utility map” is not described in the current application’s specification. Based on the Applicant’s remark that this limitation is described in the application’s specification [0057]-[0067] which describe the ML model 708 generates a map that show projected utility load demand at distinct location within the regions which is different of “recommending at least one charge to the existing utility …”. Wherein, “the projection of utility load demand at distinct location within the regions” means a forecast of future outcome, while the “recommending at least one charge to the existing utility” means a suggestion of charge to specific utility infrastructure with a decision maker.
Therefore, the amendment with “recommending at least one charge to the existing utility infrastructure based on the utility map” presents a new matter.
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 1-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The independent claims 1 & 15 as shown:
Claim 1. A computer-implemented method of determining a utility load demand based on an electric vehicle (EV) charging forecast, the method comprising:
providing one or more vehicle parameters for a plurality of areas and one or more location parameters associated with the plurality of areas to a machine learning (ML) model, wherein the ML model is at least one of a neural network ML model and a support vector ML model;
iteratively training the ML model to identify relationships between the one or more vehicle parameters, the one or more location parameters, and historical utility data associated with the plurality of areas, wherein such iterative training improves the accuracy of the ML model;
receiving, from a user via a user interface, a target area and a future target date;
providing the target area and the future target date to the trained ML model;
obtaining one or more target vehicle parameters and one or more target location parameters for the target area;
providing the one or more target vehicle parameters and the one or more target location parameters to the trained ML model;
determining, via the trained ML model, an EV charging forecast for the target area at the future target date, the EV charging forecast representing an expected quantity of EV chargers to be installed in the target area by the future target date;
projecting, via the trained ML model, a utility load demand within the target area at the future target date based on the EV charging forecast;
generating a utility map illustrating capabilities of an existing utility infrastructure within the target area relative to the projected utility load demand; and
recommending at least one change to the existing utility infrastructure based on the utility map.
Claim 15. (Currently Amended) A system comprising: at least one memory for storing computer-executable instructions; and at least one processor for executing the instructions stored on the memory, wherein execution of the instructions programs the at least one processor to perform operations comprising:
providing one or more vehicle parameters for a plurality of areas and one or more location parameters associated with the plurality of areas to a machine learning (ML) model, wherein the ML model is at least one of a neural network ML model and a support vector ML model;
iteratively training the ML model to identify relationships between the one or more vehicle parameters, the one or more location parameters, and historical utility data associated with the plurality of areas, wherein such iterative training improves the accuracy of the ML model;
receiving, from a user via a user interface, a target area and a future target date;
providing the target area and the future target date to the trained ML model;
obtaining one or more target vehicle parameters and one or more target location parameters for the target area;
providing the one or more target vehicle parameters and the one or more target location parameters to the trained ML model;
determining, via the trained ML model, an EV charging forecast for the target area at the future target date, the EV charging forecast representing an expected quantity of EV chargers to be installed in the target area by the future target date;
projecting, via the trained ML model, a utility load demand within the target area at the future target date based on the EV charging forecast;
generating a utility map illustrating capabilities of an existing utility infrastructure within the target area relative to the projected utility load demand; and
recommending at least one change to the existing utility infrastructure based on the utility map.
101 Analysis - Step 1: Statutory category – Yes
The claims recite a method including at least one step, and a system which fall within one of the four statutory categories. MPEP 2106.03
101 Analysis - Step 2A Prong one evaluation: Judicial Exception – Yes – Mental processes.
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is to be analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitutes judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be the “mental processes” which can be performed in the human mind or by a human using a pen and paper. See MPEP 2106.04(a)(2)(III)
The claims recite the bolded limitation above, as drafted, are simple processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind but for the recitation of “a computer-implemented”, “a machine learning (ML) model”; and “a user interface” in claim 1, and recitation of a system comprises at least a “memory” and a “processor” in claim 15. There is nothing in the claim elements precludes the step from practically being performed in the mind. For example, but for the “a computer-implemented”, “a machine learning (ML) model”; “a user interface”; “a memory”, and “a processor” language, the claims encompass a person looking at data collected, determining and forming a simple prediction and recommendation. The mere nominal recitation of by “computer-implemented”, “ML model”, “a user interface”; “a memory”, and “a processor” do not take the claims limitations out of the mental process grouping.
Thus, the claim recites a mental process.
101 Analysis - Step 2A Prong two evaluation: Practical Application – No
In Step 2A, Prong two of the 2019 PEG, a claim is to be evaluated whether, as a whole, it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as: merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing bolded limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claim recites the steps of “providing one or more vehicle parameter …; “iteratively training the ML model …; receiving …a target area …; providing the target area …; obtaining one or more target …”, and etc. as a general means of gathering vehicle parameters at location information; and determining the EV charging forecast, and et. In claims 1 & 15 that amount to mere data gathering, which is a form of insignificant extra-solution activity. The steps of projecting, via the trained ML model, a utility load demand at target area; “generating a utility map …”; and recommending at least one change to the existing utility …” which amounts to mere post solution displaying, which is a form of insignificant extra-solution activity. The “a computer-implemented”, “a machine learning (ML) model”; and “a user interface”; “a memory”; and “a processor” merely describes how to generally “apply” the otherwise mental judgements using a generic computer and software.
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.
101 Analysis - Step 2B evaluation: Inventive concept – No
In Step 2B of the 2019 PEG, a claim is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer and generic software. The same analysis applies here in 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the gathering data steps and the post solution steps were considered to be insignificant extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. 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 the gathering data steps and the post solution steps are well‐understood, routine, and conventional functions when it is claimed in a merely generic manner. Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer.
Thus, the claims are ineligible.
Dependent Claims
Dependent claims(s) 2-14 & 16-28 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application [provide concise explanation]. Therefore, dependent claims are not patent eligible under the same rationale as provided for in the rejection of claim 1 and claim 15
Therefore, claim(s) 1-28 is/are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
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) 1-28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai (20130222158) in view of Sun (20160300170).
With regard to claim 1, Dai discloses a computer-implemented method of determining a utility load demand based on an electric vehicle (EV) charging forecast, the method comprising:
providing one or more vehicle parameters for a plurality of areas and one or more location parameters associated with the plurality of areas (traffic sensor 102 provide vehicle type to a modality detection module 104 which classifies the sensor data, see [0028]-[0029]+);
identify relationships between the one or more vehicle parameters the one or more location parameters and historical utility data associated with the plurality of areas, (the modality detection module 104 continue collecting the sensor to classify the vehicle type, see [0029]+)
an EVR evaluator 130 receives the vehicle flow characterizer 112, traffic information, locations information, EV density, constraints, charge depletion models to determine in real time demand for EV charging, see [0034]-[0036]+ ;
receiving target area and target data too determine EV charging forecast for the target area and target day, see [0050]-[0055]+
projecting a utility load demand within the target area at the future target date based on the EV charging forecast (the charge demand module uses scheduling module to provide all types of stations available to meet charging requirement, see [0036]-[0040]+).
Dai fails to teach using Machine learning model for:
training the data, and a user interface for receiving target location and target day; determining, via the trained ML model, an EV charging forecast for the target area at the future target date, the EV charging forecast representing an expected quantity of EV chargers to be installed in the target area by the future target date; generating a utility map illustrating capabilities of an existing utility infrastructure within the target area relative to the projected utility load demand; and recommending at least one change to the existing utility infrastructure based on the utility map.
Sun discloses a system comprises an input device for receiving target location and target day (see [0018], a memory 112 including prediction model, optimizer, output module and database using machine learning algorithms to determine the optimal size of each charging station, see [Fig. 1, Fig. 2 [0033]+). Fig. 2 shows a method steps which including step of predict charging demand distribution over a set of locations, see [0027] (equivalent to “determining an EV charging forecast for the target area at the future target data); and a final step 210 for an output module 123 presenting the solution of the estimation on a display image (a map and optimal locations of charging stations) that displayed on output device 104. Each optimal location represents the number of charging piles to be installed at that particular station (see [0043]+) which meets the scope of “generating a utility map illustrating capabilities of an existing utility infrastructure within the target area relative to the projected utility load demand; and recommending at least one change to the existing utility infrastructure based on the utility map”.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify by using the machine learning algorithm for training sensor, and the user interface for receiving the target area and target day; determining an EV charging forecast for the target area at the future target date, the EV charging forecast representing an expected quantity of EV chargers to be installed in the target area by the future target date; generating a utility map illustrating capabilities of an existing utility infrastructure within the target area relative to the projected utility load demand; and recommending at least one change to the existing utility infrastructure based on the utility map as taught by Sun. The combination of Dai and Sun is an adapted system for providing EV charging more effectively.
.
With regard to claim 2, Dai teaches that the method of claim 1, wherein obtaining the one or more target vehicle parameters and the one or more target location parameters for the target area includes retrieving at least a portion of the one or more target vehicle parameters and the one or more target location parameters from at least one database (the location database 120 includes dta regarding key locations in the covered geographical area, see [0034]+).
With regard to claim 3, Sun teaches that the method of claim 1, wherein obtaining the one or more target vehicle parameters and the one or more target location parameters for the target area includes receiving, via the user interface at least a portion of the one or more target vehicle parameters and the one or more target location parameters. (an input device 102 (touchpad, microphone, and etc., see [0018]+)
With regard to claim 4, Dai teaches that the method of claim 1, wherein the one or more vehicle parameters includes a rate of consumer EV adoption within the target area (optimizing EV charging infrastructure, determining an optimal number of location charging station, see [0025]+).
With regard to claim 5, Dai teaches that the method of claim 1, wherein the one or more vehicle parameters includes a rate of commercial EV adoption within the target area (charger are deployed where needed to selected locations to equip locations as indicated, e.g., commercial EV charging stations, see [0025]+).
With regard to claim 6, Sun teaches that the method of claim 1, further comprising: detecting a real-time change to the one or more target vehicle parameters and/or the one or more target location parameters; providing the real-time change to the trained ML model to improve the accuracy of the trained ML model; updating, via the trained ML model, the EV charging forecast for the target area at the future target date; and updating, via the trained ML model, the utility load demand within the target area (the charging demand for EVs are shown in Fig.4, [0030]-[0031]+).
With regard to claims 7-8, Dai teaches that the method of claim 6, wherein detecting the real-time change to the one or more target location parameters includes detecting an EV charger location and/or potential EV charger location has been added or removed within the target area (optimize placement and size of chaging stations at candidate locations, see [0022]-[0025]+).
With regard to claim 9, Dai teaches that the method of claim 1, wherein the target area is one of a state, a city, a town, a zip code, or a neighborhood (estimate spatial-temporal demand for EC charging in a geographical area or region, wherein the area or region has multiple zones, see [0026]+).
With regard to claim 10, Dai teaches that the method of claim 1, wherein the target area is a user-defined region (Charging demand is based on EV user range anxiety, such a selected charge time threshold, and etc., see [0039]-[0040]+).
With regard to claim 11, Sun teaches that the method of claim 10, further comprising: detecting, via the user interface, a real-time change to the target area; providing the real-time change to the trained ML model to improve the accuracy of the trained ML model; updating, via the trained ML model, the EV charging forecast for the target area at the future target date; and updating, via the trained ML model, the utility load demand within the target area (the charging demand for EVs are shown in Fig.4, [0030]-[0031]+).
With regard to claim 12, Dai teaches that the method of claim 1, wherein the one or more target vehicle parameters include information associated with a number of EVs located in the target area (determining an optimal number of and location for EV charging station, see [0025]+).
With regard to claim 13, Dai teaches that the method of claim 1, wherein the one or more target location parameters include information associated with potential EV charger locations within the target area *see [0025]-[0026]+).
With regard to claim 14, Dai teaches that the method of claim 1, wherein the one or more target location parameters include information associated with existing EV charger locations within the target area (the EV charge depletion database 118 models, see [0033]+).
With regard to claim 15, Dai discloses a system comprising at least one memory for storing computer-executable instructions; and at least one processor for executing the instructions stored on the memory (see Fig. 1A, Fig. 1B & Fig. 2), wherein execution of the instructions programs the at least one processor to perform operations comprising:
providing one or more vehicle parameters for a plurality of areas and one or more location parameters associated with the plurality of areas (traffic sensor 102 provide vehicle type to a modality detection module 104 which classifies the sensor data, see [0028]-[0029]+);
identify relationships between the one or more vehicle parameters the one or more location parameters and historical utility data associated with the plurality of areas, (the modality detection module 104 continue collecting the sensor to classify the vehicle type, see [0029]+)
an EVR evaluator 130 receives the vehicle flow characterizer 112, traffic information, locations information, EV density, constraints, charge depletion models to determine in real time demand for EV charging, see [0034]-[0036]+ ;
receiving target area and target data too determine EV charging forecast for the target area and target day, see [0050]-[0055]+
projecting a utility load demand within the target area at the future target date based on the EV charging forecast (the charge demand module uses scheduling module to provide all types of stations available to meet charging requirement, see [0036]-[0040]+).
Dai fails to teach using Machine learning model for:
training the data, and a user interface for receiving target location and target day; determining, via the trained ML model, an EV charging forecast for the target area at the future target date, the EV charging forecast representing an expected quantity of EV chargers to be installed in the target area by the future target date; generating a utility map illustrating capabilities of an existing utility infrastructure within the target area relative to the projected utility load demand; and recommending at least one change to the existing utility infrastructure based on the utility map.
Sun discloses a system comprises an input device for receiving target location and target day (see [0018], a memory 112 including prediction model, optimizer, output module and database using machine learning algorithms to determine the optimal size of each charging station, see [Fig. 1, Fig. 2 [0033]+). Fig. 2 shows a method steps which including step of predict charging demand distribution over a set of locations, see [0027] (equivalent to “determining an EV charging forecast for the target area at the future target data); and a final step 210 for an output module 123 presenting the solution of the estimation on a display image (a map and optimal locations of charging stations) that displayed on output device 104. Each optimal location represents the number of charging piles to be installed at that particular station (see [0043]+) which meets the scope of “generating a utility map illustrating capabilities of an existing utility infrastructure within the target area relative to the projected utility load demand; and recommending at least one change to the existing utility infrastructure based on the utility map”.
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to modify by using the machine learning algorithm for training sensor, and the user interface for receiving the target area and target day; determining an EV charging forecast for the target area at the future target date, the EV charging forecast representing an expected quantity of EV chargers to be installed in the target area by the future target date; generating a utility map illustrating capabilities of an existing utility infrastructure within the target area relative to the projected utility load demand; and recommending at least one change to the existing utility infrastructure based on the utility map as taught by Sun. The combination of Dai and Sun is an adapted system for providing EV charging more effectively.
With regard to claim 16, Dai teaches that the system of claim 15, wherein obtaining the one or more target vehicle parameters and the one or more target location parameters for the target area includes retrieving at least a portion of the one or more target vehicle parameters and the one or more target location parameters from at least one database (the location database 120 includes dta regarding key locations in the covered geographical area, see [0034]+).
With regard to claim 17, Sun teaches that the system of claim 15, wherein obtaining the one or more target vehicle parameters and the one or more target location parameters for the target area includes receiving, via the user interface, at least a portion of the one or more target vehicle parameters and the one or more target location parameters (an input device 102 (touchpad, microphone, and etc., see [0018]+)
With regard to claim 18, Dai teaches that the system of claim 15, wherein the one or more vehicle parameters includes a rate of consumer EV adoption within the target area (optimizing EV charging infrastructure, determining an optimal number of location charging station, see [0025]+).
With regard to claim 19, Dai teaches that the system of claim 15, wherein the one or more vehicle parameters includes a rate of commercial EV adoption within the target area (charger are deployed where needed to selected locations to equip locations as indicated, e.g., commercial EV charging stations, see [0025]+).
With regard to claim 20, Sun teaches that the system of claim 15, wherein execution of the instructions programs the at least one processor to perform operations further comprising: detecting a real-time change to the one or more target vehicle parameters and/or the one or more target location parameters; providing the real-time change to the trained ML model to improve the accuracy of the trained ML model; updating, via the trained ML model, the EV charging forecast for the target area at the future target date; and updating, via the trained ML model, the utility load demand within the target area (the charging demand for EVs are shown in Fig.4, [0030]-[0031]+).
With regard to claims 21-22, Dai teaches that the system of claim 20, wherein detecting the real-time change to the one or more target location parameters includes detecting an EV charger location and/or potential charger location has been added or removed within the target area (optimize placement and size of chaging stations at candidate locations, see [0022]-[0025]+).
With regard to claim 23, Dai teaches that the system of claim 15, wherein the target area is one of a state, a city, a town, a zip code, or a neighborhood (estimate spatial-temporal demand for EC charging in a geographical area or region, wherein the area or region has multiple zones, see [0026]+).
With regard to claim 24, Dai teaches that the system of claim 15, wherein the target area is a user-defined region (Charging demand is based on EV user range anxiety, such a selected charge time threshold, and etc., see [0039]-[0040]+).
With regard to claim 25, Sun teaches that the system of claim 24, wherein execution of the instructions programs the at least one processor to perform operations further comprising: detecting, via the user interface, a real-time change to the target area; providing the real-time change to the trained ML model to improve the accuracy of the trained ML model; updating, via the trained ML model, the EV charging forecast for the target area at the future target date; and updating, via the trained ML model, the utility load demand within the target area (the charging demand for EVs are shown in Fig.4, [0030]-[0031]+).
With regard to claim 26, Dai teaches that the system of claim 15, wherein the one or more target vehicle parameters include information associated with a number of EVs located in the target area (determining an optimal number of and location for EV charging station, see [0025]+).
With regard to claim 27, Dai teaches that the system of claim 15, wherein the one or more target location parameters include information associated with potential EV charger locations within the target area see [0025]-[0026]+).
With regard to claim 28, Dai teaches that the system of claim 15, wherein the one or more target location parameters include information associated with existing EV charger locations within the target area (the EV charge depletion database 118 models, see [0033]+).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NGA X NGUYEN whose telephone number is (571)272-5217. The examiner can normally be reached M-F 5:30AM - 2:30PM.
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, JELANI SMITH can be reached at 571-270-3969. 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.
NGA X. NGUYEN
Examiner
Art Unit 3662
/NGA X NGUYEN/Primary Examiner, Art Unit 3662