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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-15 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Rosas-Maxemin et al. (US 2020/0349617) in view of Gao et al. (CN111144372A) and further in view of Nakhjavani (US 2019/0019407).
Regarding claim 1, the prior art reference discloses a computer-implemented method for performing vehicle spot dispatch (Fig. 5, [0102]: a method for implementing a parking identification system including identifying available listing locations using machine learning and image data) comprising:
analyzing, via one or more processors of the remote computing device, an image of a parking area using a machine learning model to identify an available parking space (Fig. 5, 510 ([0104]; process the captured image data of one or more listing locations using a camera, processes 510-570 are implemented by using the application server, 230; thus, server, 230, is a remote computing device [0102]), 520 ([0105]: determine available listing locations based on image data using machine learning); [0049]: Using machine learning algorithms, a neural network may be trained to identify listing locations. Machine learning algorithms are explained in further detail in the discussion of FIG. 4; [0058] In some examples, the determination of features within geographic area 304, such as parking space availability);
transmitting, via one or more processors of the remote computing device, an identification of the identified available parking space to a client computing device of the user (Fig. 5, 550; [0108]: an address and/or coordinates corresponding to the listing location. In response to securing the listing location, the first wireless device receives a signal within the geographic area, the signal including an identifier; [0109]), processes 510-570 are implemented by using the application server, 230; thus, server, 230, is a remote computing device [0102]);
identifying, via one or more processors of the remote computing device, a vehicle corresponding to a user using a trained neural network ([0008]: identifying, by the computer vision model of the computerized vehicle management system (which is the application server and is a remote computing device), one or more vehicle specific parameters associated with the vehicle; [0110]: using one or more cameras at an entrance to the parking facility, a vehicle's information is recorded at an entrance to a parking facility, including license plate information, a VIN, or other vehicle-specific information such as color, model, make, type, and/or the like. Such information may be processed locally using ML…other parameters such as the name registered for the vehicle, payment information, or other relevant information may be inputted automatically; [0079]: a machine learning (ML) model may be trained/tuned based on training data collected from positive recognition, false recognition, and/or other criteria. In some aspects, the ML model may be a deep neural network, Bayesian network, and/or the like and/or combinations thereof; [0082]; an ML model neural network includes neural network modules that comprise feed-forward computation graphs with input nodes, hidden layers, and output nodes).
Rosas-Maxemin differs from the claimed invention in that it does not explicitly disclose the trained neural network for identifying the vehicle is a ResNet having at least 50 layers. Thus, whether single-stage or not, the Gao reference teaches the method utilizes deep learning to identify image of the vehicle. The vehicle model recognition is a convolutional neural network, and also utilize Resnet-50 (Abstract; [0010, 0058, 0085]. It would have been within the level of one skilled in the art, before the effective filing date of the claimed invention, to utilize the image identification as the ResNet having at least 50 layers (ResNet-50) to implement Rosas-Maxemin’s process for identifying the vehicle information. The proposed implementation would allow the prior art “identifying” step to be performed by a highly accurate and computationally efficient image recognition and classification architecture, with a reasonable expectation of success.
Rosas-Maxemin discloses that the listing location is an electric vehicle (EV) charging station [0076]. Rosas-Maxemin does not disclose the identifying an electric vehicle parking space from the image. Nakhjavani discloses a real-time parking lot analysis and management comprising a camera for obtaining an image of the real-time parking lot to detected occupied and available parking spaces as well as identifying the parking space as EV parking spaces or handicap parking spaces [0041-0042, 0050, 0056-00570069]. It would have been within the level of one skilled in the art, before the effective filing date of the claimed invention, to utilize the identification of the electric vehicle parking space to implement Rosas-Maxemin’s process for identifying the electric vehicle parking space. The proposed implementation would allow the prior art “identifying” step to be performed by a highly accurate and computationally efficient image recognition and classification architecture, with a reasonable expectation of success.
Regarding claim 2: Rosas-Maxemin discloses the parking area is a mixed-use parking area [0047] ;(vehicle parking locations, including a lot, garage (commercial or residential), and/or other location with a space suitable for occupation, including parking for a vehicle).
Regarding claim 3: Rosas-Maxemin discloses the vehicle is an electric vehicle [0076, 0151].
Regarding claim 4: Rosas-Maxemin discloses analyzing the image of the parking area using the machine learning model to identify the available parking space includes identifying a parking location nearby a location of the user [0011, 0047, 0087].
Regarding claim 5, Rosas-Maxemin further discloses “receiving the image of the parking area from a camera capture device stationed in the parking area” (Fig. 3, [0072]: camera 310 overlooks a lot within geographic area 304; [0104] During a process 510, image data of one or more listing locations is captured using at least one camera. Image data may be captured as discussed above with respect to FIG. 3. The image data may include portions of one or more listing locations).
Regarding claim 6: Rosas-Maxemin discloses receiving a parking space request from the user; and enqueueing the parking space request in a parking space request queue [0017, 0053, 0065, 0076].
Regarding claim 7: Rosas-Maxemin discloses transmitting the identification of the identified parking space includes dequeueing the parking space request from the parking space request queue (departure from the parking lot; thus, the request list is removed so that other vehicle’s request may be placed on the list) [0113, 0131-0133].
Regarding claims 8 and 15: See claim 1 above.
Regarding claim 9: See claim 2 above.
Regarding claim 10: See claim 3 above.
Regarding claims 11 and 17: See claim 4 above.
Regarding claims 12 and 18: See claim 5 above.
Regarding claims 13 and 19: See claim 6 above.
Regarding claims 14 and 20: See claim 7 above.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Rosas-Maxemin et al. (US 2020/0349617) in view of Gao et al. (CN111144372A) as applied to claim 15 above and further in view of Sadeghi (US 2017/0323227).
Regarding claim 16: Rosas-Maxemin in view of Gao does not disclose the removal of a user’s command after the command is expired. Sadeghi discloses the computer to automatically remove the user command when the user command is expired [0102]. Sadeghi discloses a parking space management system by using artificial intelligence and computer vision comprising the removal of a user command/request once a predetermined time has passed [0102]. It would have been within the level of one skilled in the art, before the effective filing date of the claimed invention, to utilize the parking management system of Sadeghi as the neural network [0042] to implement Rosas-Maxemin’s and Gao’s process for identifying the vehicle information. The proposed implementation would allow the system to conveniently and effectively free up the queue or request list so that additional requests may be made.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 10,789,846 and claims 1-20 of U.S. Patent No. 11,348,461. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 of the patents contain all the limitations of claims 1-20 of the instant application. The same limitations are: “enqueueing and dequeueing the parking space requests; analyzing an image of a parking area to identify an available parking space; and transmitting an identification of the identified available parking space to the user’.
Response to Arguments
Applicant’s arguments, see Applicant Arguments/Remarks, filed 03/09/26, with respect to the rejection(s) of claim(s) 1-20 under Maxemin et al. (US 2020/0349617) in view of Gao et al. (CN111144372A) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made further in view of Nakhjavani (US 2019/0019407).
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 TOAN NGOC PHAM whose telephone number is (571)272-2967. The examiner can normally be reached M - F (7 AM - 3:30 PM).
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/TOAN N PHAM/Primary Examiner, Art Unit 2685 04/01/26