DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Oath/Declaration
Oath/Declaration as filed on February 5, 20025 is noted by the Examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, and 8-10 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Rosas-Maxemin et al., U.S. Patent Application Publication 2020/0349617 A1 (hereinafter Rosas-Maxemin).
Regarding claim 1, Rosas-Maxemin teaches a vehicle management system that manages a vehicle in a predetermined area, the vehicle management system comprising: (200 FIG. 1-4, and 11-12 paragraph[0043] of Rosas-Maxemin teaches FIG. 2 is a simplified diagram of a distributed computing system 200; and in some aspects, as shown in FIG. 2, system 200 includes wireless device 210, vehicle device 220, application servers 230, map database 240, and vehicle 250; and in some examples, media device 210 and/or vehicle device 220 may correspond to one or more of computing devices 110, 140, 170 and may be in communication with one another using network 101, and See also at least ABSTRACT, and paragraphs[0008]-[0009], [0035]-[0042], [0052], [0061], and [0142]-[0174] of Rosas-Maxemin (i.e., Rosas-Maxemin teaches a distributed computing system having a database used by a vehicle management system for automatically identifying vehicle specific parameters associated with a vehicle and facilitating parking and payment for a vehicle at a location within a geographic area)) processing circuitry; and a storage configured to store a machine learning model for estimating a position of a vehicle shown in an image, wherein the processing circuitry is configured to: acquire a target-specialized parameter from a parameter providing apparatus (120, 150, and 180; and 130 FIG. 1-4, and 12 paragraph[0036] of Rosas-Maxemin teaches in some aspects, computing device 110 includes a control unit 120 coupled to memory 130; computing device 140 includes a control unit 150 coupled to memory 160; and computing device 170 includes a control unit 180 coupled to memory 190; each of control units 120, 150, and/or 180 may control the operation of its respective computing device 110, 140, and/or 170; in some examples, control units 120, 150, and/or 180 may each include one or more processors, central processing units (CPUs), graphical processing units (GPUs), virtual machines, microprocessors, microcontrollers, logic circuits, hardware finite state machines (FSMs), digital signal processors (DSPs) application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), and/or the like and/or combinations thereof; in some examples, memory 130 may be used to store one or more applications and one or more data structures, such as an application 132 and data structure 134; and in some examples, memory 160 may be used to store one or more applications and one or more data structures, such as an application 162 and data structure 164, and memory 190 may be used to store one or more applications and one or more data structures, such as an application 192 and data structure 194, and See also at least ABSTRACT, and paragraphs[0008]-[0009], [0035], [0037]-[0043], [0048], [0052], [0058], [0061], [0075], [0083]-[0087], [0110], and [0142]-[0174] of Rosas-Maxemin (i.e., Rosas-Maxemin teaches a distributed computing system, which has the database as well as control units and memory capable of storing a machine learning (ML) model, used by the vehicle management system for automatically identifying vehicle specific parameters associated with the vehicle and facilitating parking and payment for the vehicle at the location within the geographic area, wherein the vehicle specific parameters are within data structures and are used by the ML model to determine a listing location for the vehicle for parking, and wherein the ML model is on a device along with processors that processes the vehicle specific parameters)), the target-specialized parameter being a parameter of the machine learning model trained with a focus on a category of a target vehicle; apply the target-specialized parameter to the machine learning model stored in the storage to acquire a target-specialized machine learning model specialized in the category of the target vehicle; acquire an image captured by a camera installed in the predetermined area and showing the target vehicle; and estimate a position of the target vehicle based on the image and the target-specialized machine learning model (FIG. 1-4, and 12 paragraph[0083] of Rosas-Maxemin teaches in some aspects, once an ML model is trained, the ML model may receive one or more inputs, e.g., one or more images captured by external sensors or cropped portions of one or more images captured by one or more sensors, and classify those inputs as having a particular numeric affinity for each class for which the network was trained; in some examples, a ML model may be specific to a particular listing location or group of listing locations, such as a parking lot; in some examples, the ML model may be specific to a parking lot at specific times, such as a busy grocery lot during the weekend; in some examples, a ML model may be trained to identify certain features of vehicles, such as, identify certain aspects of the vehicle such as the make, model, year, color, shape, and if any, customizations, damage and plates; and some advantages of disclosed embodiments include, identification of vehicles even when a license plate cannot be recognized, and See also at least ABSTRACT, and paragraphs[0008]-[0009], [0035]-[0043], [0048], [0052], [0058], [0061], [0075], [0078]-[0082], [0084]-[0087], [0110], and [0142]-[0174] of Rosas-Maxemin (i.e., Rosas-Maxemin teaches a distributed computing system, which has the database as well as control units and memory capable of storing the ML model, used by the vehicle management system for automatically identifying vehicle specific parameters associated with the vehicle and facilitating parking and payment for the vehicle at the location within the geographic area, wherein the vehicle specific parameters are within data structures and are used by the ML model to determine a listing location for the vehicle for parking, wherein the ML model is on a device along with processors that processes the vehicle specific parameters, and wherein the machine learning model receives one or more images, which are from cameras that are on one or more vehicles or placed in a vantage point, and is trained to identify certain features of the vehicle as well as orientation of how the vehicle is parked)).
Regarding claim 3, Rosas-Maxemin teaches the vehicle management system according to claim 1, wherein the processing circuitry is further configured to manage traveling of the target vehicle in the predetermined area based on the estimated position of the target vehicle (FIG. 1-4, 8, and 12 paragraph[0135] of Rosas-Maxemin teaches during a process 850, the first vehicle is routed to an exit of the parking lot; in some examples, directions routing the first vehicle to the exit of the parking lot are provided on a navigation application such as discussed above with respect to FIG. 2; and the first vehicle may be routed to an exit and moved autonomously as discussed above with respect to FIGS. 2-4, and See also at least ABSTRACT, and paragraphs[0008]-[0009], [0035]-[0043], [0048], [0052], [0058], [0061], [0075], [0078]-[0082]-[0087], [0110], [0129]-[0134], and [0142]-[0174] of Rosas-Maxemin (i.e., Rosas-Maxemin teaches a distributed computing system, which has the database as well as control units and memory, used by the vehicle management system for automatically identifying vehicle specific parameters associated with the vehicle and facilitating parking and payment for the vehicle at the location within the geographic area, wherein the vehicle specific parameters are within data structures and are used by a machine learning (ML) model to determine a listing location for the vehicle for parking, wherein the ML model is on a device along with processors that processes the vehicle specific parameters, wherein the machine learning model receives one or more images, which are from cameras that are on one or more vehicles or placed in a vantage point, and is trained to identify certain features of the vehicle as well as orientation of how the vehicle is parked, and processors wherein at least a processor, which runs executable code stored on computer readable memory, of one of the control units is capable of routing the vehicle to an exit within the geographic area autonomously)).
Regarding claim 8, Rosas-Maxemin teaches the vehicle management system according to claim 1,wherein the parameter providing apparatus retains a plurality of types of target-specialized parameters that are respective parameters of machine learning models respectively trained with focuses on a plurality of categories, and the processing circuitry is further configured to selectively acquire the target-specialized parameter specialized in the category of the target vehicle from among the plurality of types of target-specialized parameters (FIG. 1-4, and 12 paragraph[0061] of Rosas-Maxemin teaches in some aspects, geographic area 304 includes a parking lot; in some examples, wireless device 306 enters geographic area 304 with vehicle 350; in some examples, a pass may be added to the first software application, such as APPLE WALLET; once the pass is added, wireless device may be presented with an option to download a second software application, such as a parking application (e.g., PIED PARKER); in some examples, the second software application may already be installed on wireless device 306 and deliver a notification to the user prompting them to save a new pass to their device or open an existing one; in some examples, at the entrance to the parking lot there is a kiosk or other fixed device that uses a short-range wireless technology, such as NFC, BLUETOOTH low energy (BLE) and/or the like, which, when in proximity or direct contact with wireless device 306, causes wireless device 306 to prompt a pass to be added via the first software application; in some examples, the fixed device includes an NFC reader, RFID tag reader, a facial recognition device, retina scanner, a fingerprint reader, barcode scanner, and/or the like and/or combinations thereof; in some examples, at the entrance/exit to the parking lot there are sensors, such as cameras and motion sensors, that may identify one or more vehicle specific parameters; in some examples, one or more of these one or more vehicle specific parameters may be used to identify the vehicle and associate the vehicle with a payment profile; in some examples, vehicle specific parameters may be identified by using machine learning as discussed below with respect to FIG. 4; the one or more vehicle specific-parameters may include, for example, a license plate number, VIN, make, model, an appearance of the vehicle (e.g., decals, vehicle damage, vehicle customizations, such as spoilers, after-market additions), by one or more identifiers broadcast by a device within the vehicle, such as the stereo system projecting a MAC address, RFID chip (e.g., an RFID on a license plate), auditory signatures such as engine noise, or identification of a driver, passenger, or other vehicle occupant such as by using facial identification, stickers (e.g., QR code), BLE tracker, and/or the like; and in some examples, a sticker may include a QR code that is colored to look like a logo, such as the gray and orange logo of PIED PARKER. The QR code may identify a vehicle as corresponding to a particular user, or, in some cases, multiple drivers corresponding to one vehicle. and the like, and See also at least ABSTRACT, and paragraphs[0008]-[0009], [0035]-[0043], [0048], [0052], [0058], [0075]-[0083], [0084]-[0087], [0110], and [0142]-[0174] of Rosas-Maxemin (i.e., Rosas-Maxemin teaches a distributed computing system, which has the database as well as control units and memory, used by the vehicle management system for automatically identifying vehicle specific parameters associated with the vehicle and facilitating parking and payment for the vehicle at the location within the geographic area, wherein the vehicle specific parameters are within data structures and are used by a machine learning (ML) model to determine a listing location for the vehicle for parking, wherein the ML model is on a device along with processors that processes the vehicle specific parameters, wherein the machine learning model receives one or more images, which are from cameras that are on one or more vehicles or placed in a vantage point, and is trained to identify certain features of the vehicle as well as orientation of how the vehicle is parked, wherein external sensors (e.g., cameras) also having processors are able to identify vehicle specific parameters through communication with sensors of the vehicle, and wherein the machine learning model is trained to identify even certain aspects of the vehicle including customizations)).
Regarding claim 9, Rosas-Maxemin teaches the vehicle management system according to claim 1, wherein the target-specialized parameter is a parameter of the machine learning model trained with a focus not only on the category of the target vehicle but also on a vehicle appearance customized by a user (FIG. 1-4, and 12 paragraph[0083] of Rosas-Maxemin teaches in some aspects, once an ML model is trained, the ML model may receive one or more inputs, e.g., one or more images captured by external sensors or cropped portions of one or more images captured by one or more sensors, and classify those inputs as having a particular numeric affinity for each class for which the network was trained; in some examples, a ML model may be specific to a particular listing location or group of listing locations, such as a parking lot; in some examples, the ML model may be specific to a parking lot at specific times, such as a busy grocery lot during the weekend; in some examples, a ML model may be trained to identify certain features of vehicles, such as, identify certain aspects of the vehicle such as the make, model, year, color, shape, and if any, customizations, damage and plates; and some advantages of disclosed embodiments include, identification of vehicles even when a license plate cannot be recognized, and See also at least ABSTRACT, and paragraphs[0008]-[0009], [0035]-[0043], [0048], [0052], [0058], [0061], [0075]-[0083], [0084]-[0087], [0110], and [0142]-[0174] of Rosas-Maxemin (i.e., Rosas-Maxemin teaches a distributed computing system, which has the database as well as control units and memory, used by the vehicle management system for automatically identifying vehicle specific parameters associated with the vehicle and facilitating parking and payment for the vehicle at the location within the geographic area, wherein the vehicle specific parameters are within data structures and are used by a machine learning (ML) model to determine a listing location for the vehicle for parking, wherein the ML model is on a device along with processors that processes the vehicle specific parameters, wherein the machine learning model receives one or more images, which are from cameras that are on one or more vehicles or placed in a vantage point, and is trained to identify certain features of the vehicle as well as orientation of how the vehicle is parked, wherein external sensors (e.g., cameras) also having processors are able to identify vehicle specific parameters through communication with sensors of the vehicle, and wherein the machine learning model is trained to identify even certain aspects of the vehicle including customizations)).
Regarding claim 10, Rosas-Maxemin teaches a vehicle management method for managing a vehicle in a predetermined area by a computer, the vehicle management method comprising (200 FIG. 1-4, 7, and 11-12 paragraph[0043] of Rosas-Maxemin teaches FIG. 2 is a simplified diagram of a distributed computing system 200; and in some aspects, as shown in FIG. 2, system 200 includes wireless device 210, vehicle device 220, application servers 230, map database 240, and vehicle 250; and in some examples, media device 210 and/or vehicle device 220 may correspond to one or more of computing devices 110, 140, 170 and may be in communication with one another using network 101, and See also at least ABSTRACT, and paragraphs[0008]-[0009], [0035]-[0042], [0050], [0061], [0075], [0114-[0128], and [0142]-[0174] of Rosas-Maxemin (i.e., Rosas-Maxemin teaches a distributed computing system having a database used by a vehicle management system for automatically identifying vehicle specific parameters associated with a vehicle and facilitating parking and payment for a vehicle at a location within a geographic area)):
acquiring a machine learning model for estimating a position of a vehicle shown in an image; acquiring a target-specialized parameter from a parameter providing apparatus (ML FIG. 1-4, and 12 paragraph[0036] of Rosas-Maxemin teaches in some aspects, computing device 110 includes a control unit 120 coupled to memory 130; computing device 140 includes a control unit 150 coupled to memory 160; and computing device 170 includes a control unit 180 coupled to memory 190; each of control units 120, 150, and/or 180 may control the operation of its respective computing device 110, 140, and/or 170; in some examples, control units 120, 150, and/or 180 may each include one or more processors, central processing units (CPUs), graphical processing units (GPUs), virtual machines, microprocessors, microcontrollers, logic circuits, hardware finite state machines (FSMs), digital signal processors (DSPs) application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), and/or the like and/or combinations thereof; in some examples, memory 130 may be used to store one or more applications and one or more data structures, such as an application 132 and data structure 134; and in some examples, memory 160 may be used to store one or more applications and one or more data structures, such as an application 162 and data structure 164, and memory 190 may be used to store one or more applications and one or more data structures, such as an application 192 and data structure 194, and See also at least ABSTRACT, and paragraphs[0008]-[0009], [0035], [0037]-[0043], [0048], [0052], [0058], [0061], [0075], [0083]-[0087], [0110], and [0142]-[0174] of Rosas-Maxemin (i.e., Rosas-Maxemin teaches a distributed computing system, which has the database as well as control units and memory capable of storing a machine learning (ML) model, used by the vehicle management system for automatically identifying vehicle specific parameters associated with the vehicle and facilitating parking and payment for the vehicle at the location within the geographic area, wherein the vehicle specific parameters are within data structures and are used by the ML model to determine a listing location for the vehicle for parking, and wherein the ML model is on a device along with processors that processes the vehicle specific parameters)), the target-specialized parameter being a parameter of the machine learning model trained with a focus on a category of a target vehicle; applying the target-specialized parameter to the machine learning model to acquire a target-specialized machine learning model specialized in the category of the target vehicle; acquiring an image captured by a camera installed in the predetermined area and showing the target vehicle; and estimating a position of the target vehicle based on the image and the target-specialized machine learning model (FIG. 1-4, and 12 paragraph[0083] of Rosas-Maxemin teaches in some aspects, once an ML model is trained, the ML model may receive one or more inputs, e.g., one or more images captured by external sensors or cropped portions of one or more images captured by one or more sensors, and classify those inputs as having a particular numeric affinity for each class for which the network was trained; in some examples, a ML model may be specific to a particular listing location or group of listing locations, such as a parking lot; in some examples, the ML model may be specific to a parking lot at specific times, such as a busy grocery lot during the weekend; in some examples, a ML model may be trained to identify certain features of vehicles, such as, identify certain aspects of the vehicle such as the make, model, year, color, shape, and if any, customizations, damage and plates; and some advantages of disclosed embodiments include, identification of vehicles even when a license plate cannot be recognized, and See also at least ABSTRACT, and paragraphs[0008]-[0009], [0035]-[0043], [0048], [0052], [0058], [0061], [0075], [0078]-[0082], [0084]-[0087], [0110], and [0142]-[0174] of Rosas-Maxemin (i.e., Rosas-Maxemin teaches a distributed computing system, which has the database as well as control units and memory capable of storing the ML model, used by the vehicle management system for automatically identifying vehicle specific parameters associated with the vehicle and facilitating parking and payment for the vehicle at the location within the geographic area, wherein the vehicle specific parameters are within data structures and are used by the ML model to determine a listing location for the vehicle for parking, wherein the ML model is on a device along with processors that processes the vehicle specific parameters, and wherein the machine learning model receives one or more images, which are from cameras that are on one or more vehicles or placed in a vantage point, and is trained to identify certain features of the vehicle as well as orientation of how the vehicle is parked)).
Potentially Allowable Subject Matter
Claims 2, and 4-7 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims, because for each of claims 2, and 4-7 the prior art references of record do not teach the combination of all element limitations as presently claimed.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDUL-SAMAD A ADEDIRAN whose telephone number is (571)272-3128. The examiner can normally be reached on Monday through Thursday, 8:00 am to 5:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amr Awad can be reached on 571-272-7764. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see https://ppair-my.uspto.gov/pair/PrivatePair. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ABDUL-SAMAD A ADEDIRAN/Primary Examiner, Art Unit 2621
Prosecution Insights