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 § 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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
Claims 1-10 recite a method (i.e. process), and claims 11-20 recite a server (i.e. machine). Therefore claims 1-20 fall within one of the four statutory categories of invention.
Independent claim 1 recites the limitations training a license plate recognition model based on multiple training data including the license plate; receiving an image of a vehicle entering in a parking lot from a [camera] installed to a parking area and extracting text information of the license plate by inputting the received image to the license plate recognition model; verifying parking area identification information of a parking area on which the vehicle parks when the [server] receives an image of the parked vehicle; completing entry by associating the parking area identification information, entry time, and recognized license plate with user profile including phone number and payment method received via a [QR-code link], or, when pre-registered, by automatically retrieving the user profile linked to the recognized license plate; and processing an exit of the vehicle related to the user profile and performing payment of parking fee based on the payment method when an exit signal is received from a [user terminal] or a license plate of an exited vehicle in the image received from the [camera] matches with the license plate of the vehicle related to the user profile. The invention and claims are drawn towards a parking system that accurately recognizes license plates and the claim limitation corresponds to certain methods of organizing human activity (managing personal interactions, behavior, relationships; commercial interactions; business relations) as evidence by limitations detailing receiving and analyzing images of a vehicle entering a parking information, user information corresponding to the user profile and/or phone number and other personal information, and performing payment of a parking fee. The claim also corresponds to mental processes (observation, evaluation, judgment, opinion) since the claim limitations also describe the observation and evaluation of parking associated or related data, and making a decision or condition (judgment/opinion) based on the observed and evaluated data. The claims also recite a license plate recognition model (interpreted as an algorithm) that amounts to mathematical concepts (mathematical relationships, formulas, equations, or calculations). The claim recites an abstract idea.
Note: The features or elements in brackets in the above Step 2A Prong One section are inserted for reading clarity, but are analyzed as “additional elements” under Step 2A Prong Two and Step 2B below.
The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: a server, a camera, a QR-code link, and a user terminal. The additional elements of the server and user terminal are computer components recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Further, QR-code link and camera amounts to generally linking the judicial exception to a particular field of use (payment & license plate recognition in vehicle parking) Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer, and generally linking the judicial exception to a particular field of use. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
Independent claim 11 recites the limitations: training a license plate recognition model based on multiple training data including the license plate; receiving an image of a vehicle entering in a parking lot from a [camera] installed to a parking area and extracting text information of the license plate by inputting the received image to the license plate recognition model; verifying parking area identification information of a parking area on which the vehicle parks when the [server] receives an image of the parked vehicle; matching user’s individual information related to the parked vehicle with the parking area identification information, an entry time and a recognized license plate to complete an entry as a [user terminal] transmits phone number or information concerning means of payment inputted through a link in the [QR code] installed to the parking area to the [server] or the [server] verifies user’s identification information stored in association with the recognized license plate; and processing an exit of the vehicle related to the [user terminal] and performing payment of parking fee based on the information concerning means of payment when an exit signal is received from the [user terminal] or a license plate of an exited vehicle in the image received from the [camera] matches with the license plate of the vehicle related to the [user terminal]. The invention and claims are drawn towards a parking system that accurately recognizes license plates and the claim limitation corresponds to certain methods of organizing human activity (managing personal interactions, behavior, relationships; commercial interactions; business relations) as evidence by limitations detailing receiving and analyzing images of a vehicle entering a parking information, user information corresponding to the vehicle and/or phone number and other personal information, and performing payment of a parking fee. The claim also corresponds to mental processes (observation, evaluation, judgment, opinion) since the claim limitations also describe the observation and evaluation of parking associated or related data, and making a decision or condition (judgment/opinion) based on the observed and evaluated data. The claims also recite a license plate recognition model (interpreted as an algorithm) that amounts to mathematical concepts (mathematical relationships, formulas, equations, or calculations). The claim recites an abstract idea.
Note: The features or elements in brackets in the above Step 2A Prong One section are inserted for reading clarity, but are analyzed as “additional elements” under Step 2A Prong Two and Step 2B below.
The judicial exception is not integrated into a practical application simply because the claims recite the additional elements of: a server, memory, processor, a camera, a QR code, and a user terminal. The additional elements of the server, memory, processor, and user terminal are computer components recited at a high-level of generality performing the above-mentioned limitations. The combination of the additional elements are no more than mere instructions to apply the judicial exception using a generic computer. Further, QR code and camera amounts to generally linking the judicial exception to a particular field of use (payment & license plate recognition in vehicle parking) Accordingly, in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply the exception using a generic computer, and generally linking the judicial exception to a particular field of use. Mere instructions to apply an exception using a generic computer cannot provide an inventive concept. Thus, when viewed as an ordered combination, nothing in the claims add significantly more (i.e. an inventive concept) to the abstract idea. The claims are not patent eligible.
Dependent claims 2 and 12 recite the limitations of storing a plurality of training data; extracting an image region corresponding to the license plate in each of the training data; identifying text of the license plate in the extracted image region; generating refined training data by adding defect images to the extracted image region; and training a preset machine learning model by inputting the refined training data and corresponding texts to the preset machine learning model such that the text is outputted when the refined training data is inputted to the preset machine learning model. The claim limitations are further direct dot the judicial exception analyzed above. Further, the preset machine learning model (interpreted as an algorithm) also corresponds to mathematical concepts. The claims are not patent eligible.
Dependent claims 7 and 17 recite the limitations that the generating the refined training data includes: adding multiple Gaussian-blur masks or multiple white occlusion masks to arbitrary local regions within the extracted image region. The limitations are further directed to the judicial exceptions analyzed above. Further, the addition of multiple Gaussian blur masks amounts to mathematical concepts since Gaussian blur applies a mathematical Gaussian function to smooth images, reduce noise, and minimize detail. The claims are not patent eligible.
Dependent claims 3-6, 8-10, 13-16, and 18-20 recite additional limitations that are further directed to the abstract idea analyzed in the rejected claims above and/or additional elements that have been analyzed in the rejected claims above. Thus, claims 3-6, 8-10, 13-16, and 18-20 are also rejected under 35 U.S.C. 101.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 10, 11, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over DeSantola (2022/0292618) in view of Lyles (2022/0230538).
Claim 1: DeSantola discloses: A method of providing a parking system using QR code based on recognition of a license plate, executed by a server, the method comprising the steps of:
(a) training a license plate recognition model based on multiple training data including the license plate; (DeSantola ¶0075 one or more trained machine learning models may be trained using image data to learn vehicle identification (license plate), vehicle order association, vehicle routing, and/or vehicle tracking associated with image data of images of a drive-thru area; ¶0049 receiving image data (e.g., frames of videos) from one or more cameras that are external to the restaurant and directed at a drive-thru, parking lot, surrounding streets, etc. and processes the image data to determine information about vehicles in the drive-thru; ¶0067 the vehicle identification tool, may identify vehicles by determining a visual indicator (e.g., license plate, make/model of the vehicle))
(b) receiving an image of a vehicle entering in a parking lot from a camera installed to a parking area and extracting text information of the license plate by inputting the received image to the license plate recognition model; (DeSantola ¶0049 receiving image data (e.g., frames of videos) from one or more cameras that are external to the restaurant and directed at a drive-thru, parking lot, surrounding streets, etc. and processes the image data to determine information about vehicles in the drive-thru; ¶0067 the vehicle identification tool, may identify vehicles by determining a visual indicator (e.g., license plate, make/model of the vehicle); ¶0050 restaurants that use a single food delivery window for the drive-thru may make use of a waiting bay and/or parking spot; ¶0075 one or more trained machine learning models may be trained using image data to learn vehicle identification (license plate), vehicle order association, vehicle routing, and/or vehicle tracking associated with image data of images of a drive-thru area)
(c) verifying parking area identification information of a parking area on which the vehicle parks when the server receives an image of the parked vehicle; (DeSantola ¶0030 outputs based on process of the image data can be assumed to assist in one or more drive-thru operations; the processed image data may be leveraged to identify vehicles in image(s), track vehicles across frames, associate pending meal orders with the vehicles, etc.; processed image frames may indicate relative locations of cameras disposed within a drive thru area and relative position of vehicles within the drive-thru area; ¶0049 drive-thru tracking system receives image data (e.g., frames of videos) from one or more cameras that are external to the restaurant and directed at a drive-thru, parking lot, surrounding streets, etc. and processes the image data to determine information about vehicles in the drive-thru; ¶0067 the vehicle identification tool, may identify vehicles by determining a visual indicator (e.g., license plate, make/model of the vehicle); vehicle identification tool may include a database of vehicles indicating an order history associated with each identified vehicle. The order association tool may associate a vehicle disposed within the drive-thru area with a pending meal order)
DeSantola in view of Lyles discloses:
(d) completing entry by associating the parking area identification information, entry time, and recognized license plate with user profile including phone number and payment method received via a QR-code link, or, when pre-registered, by automatically retrieving the user profile linked to the recognized license plate; and
DeSantola discloses completing entry by associating the parking area identification information, and recognized license plate with user profile and payment method received via a QR-code link, or, when pre-registered, by automatically retrieving the user profile linked to the recognized license plate: (DeSantola ¶0237 display may depict elements of a current meal order including one or more meal items and associated prices including a total price and may include displaying data that identifies the order on an external endpoint device, e.g., the display may show a barcode (e.g., such as a quick response (QR) code) that may be registered by an endpoint device associated with a user account indicative of the customer and customer vehicle (e.g., using an app on a customer's phone); the vehicles may be tracked as they progress through the drive-thru; QR code associated with an order associated with a particular vehicle may be automatically displayed on the display; customer may then scan the QR code with their mobile device to pay for their order; see also ¶0308). DeSantola does not explicitly disclose completing entry by associating the parking area identification information, entry time, and recognized license plate with user profile including phone number and payment method received via a QR-code link, or, when pre-registered, by automatically retrieving the user profile linked to the recognized license plate. Lyles suggests or discloses this limitation/concept: (Lyles ¶0009 detecting a license plate number in a first image, captured by an entry camera unit arranged near an entrance of the parking structure at a first time; ¶0012 read license plate numbers of vehicles entering and exiting the parking structure; write the license plate number to a data log with the vehicle's time of entry (e.g., a timestamp of an image captured by the entry camera unit and depicting the license plate number); see also ¶0013; ¶0034 scans this set of timestamped images received from the entry camera unit for a license plate number of a vehicle; ¶0051 user may submit an application form in paper, via email, or online (e.g., within a web browser or native application) to add a license plate to the list; the user may: supply a license plate number; specify a particular parking structure, parking structure complex, parking structure network, or location (e.g., an office park, a city block); start and end dates; and/or contact information (e.g., an email address, a phone number) in the application). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify DeSantola to include completing entry by associating the parking area identification information, entry time, and recognized license plate with user profile including phone number and payment method received via a QR-code link, or, when pre-registered, by automatically retrieving the user profile linked to the recognized license plate as taught by Lyles. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify DeSantola in order to manage vehicle parking in vehicle parking systems (see ¶0002 of Lyles).
(e) processing an exit of the vehicle related to the user profile and performing payment of parking fee based on the payment method when an exit signal is received from a user terminal or a license plate of an exited vehicle in the image received from the camera matches with the license plate of the vehicle related to the user profile.
DeSantola discloses tracking a vehicle through the vehicle exiting the location, but does not explicitly disclose processing an exit of the vehicle related to the user profile and performing payment of parking fee based on the payment method when an exit signal is received from a user terminal or a license plate of an exited vehicle in the image received from the camera matches with the license plate of the vehicle related to the user profile. Lyles suggests or discloses this limitation/concept: (Lyles ¶0012 capture images of entry and exit lanes in the parking structure; to read license plate numbers of vehicles entering and exiting the parking structure; and to monitor validation (e.g., payment) status of vehicles carrying these license plate numbers; ¶0014 the kiosk (or exit camera unit, local computer system, or remote computer system) detects the license plate number in an exit image captured by the exit camera unit; ¶0030 disclosing the entry and exit cameras extracting the license plate numbers and retuning them for further handling; see also ¶0044; ¶0116 detect license plate numbers in images captured by an exit camera unit installed in the parking structure and can store exit times of these license plate numbers; upon detecting a license plate number in an image captured by the exit camera unit, the system queries the data log for the entry time and status of the license plate number; calculate a time difference between the entry time and the exit time of the license plate number; implement a parking fee model to transform this time difference into a final parking cost for the license plate number; fee charged to the associated payment method for this parking period). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify DeSantola to include processing an exit of the vehicle related to the user profile and performing payment of parking fee based on the payment method when an exit signal is received from a user terminal or a license plate of an exited vehicle in the image received from the camera matches with the license plate of the vehicle related to the user profile as taught by Lyles. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify DeSantola in order to manage vehicle parking in vehicle parking systems (see ¶0002 of Lyles).
Claim 11: DeSantola discloses: A server for providing a parking system using QR code based on recognition of license plate, the server comprising:
a memory configured to store a program about a method of providing a parking service using the QR code based on the recognition of the license plate; and a processor configured to execute the program, wherein the method includes the steps of: (DeSantola ¶0005 the system including a memory and processing device coupled to the memory; ¶0075 one or more trained machine learning models may be trained using image data to learn vehicle identification (license plate), vehicle order association, vehicle routing, and/or vehicle tracking associated with image data of images of a drive-thru area; ¶0049 receiving image data (e.g., frames of videos) from one or more cameras that are external to the restaurant and directed at a drive-thru, parking lot, surrounding streets, etc. and processes the image data to determine information about vehicles in the drive-thru; ¶0067 the vehicle identification tool, may identify vehicles by determining a visual indicator (e.g., license plate, make/model of the vehicle); ¶0237 display may depict elements of a current meal order including one or more meal items and associated prices including a total price and may include displaying data that identifies the order on an external endpoint device, e.g., the display may show a barcode (e.g., such as a quick response (QR) code) that may be registered by an endpoint device associated with a user account indicative of the customer and customer vehicle (e.g., using an app on a customer's phone); the vehicles may be tracked as they progress through the drive-thru; QR code associated with an order associated with a particular vehicle may be automatically displayed on the display; customer may then scan the QR code with their mobile device to pay for their order; see also ¶0308)
(a) training a license plate recognition model based on multiple training data including the license plate; (DeSantola ¶0075 one or more trained machine learning models may be trained using image data to learn vehicle identification (license plate), vehicle order association, vehicle routing, and/or vehicle tracking associated with image data of images of a drive-thru area; ¶0049 receiving image data (e.g., frames of videos) from one or more cameras that are external to the restaurant and directed at a drive-thru, parking lot, surrounding streets, etc. and processes the image data to determine information about vehicles in the drive-thru; ¶0067 the vehicle identification tool, may identify vehicles by determining a visual indicator (e.g., license plate, make/model of the vehicle))
(b) receiving an image of a vehicle entering in a parking lot from a camera installed to a parking area and extracting text information of the license plate by inputting the received image to the license plate recognition model; (DeSantola ¶0049 receiving image data (e.g., frames of videos) from one or more cameras that are external to the restaurant and directed at a drive-thru, parking lot, surrounding streets, etc. and processes the image data to determine information about vehicles in the drive-thru; ¶0067 the vehicle identification tool, may identify vehicles by determining a visual indicator (e.g., license plate, make/model of the vehicle); ¶0050 restaurants that use a single food delivery window for the drive-thru may make use of a waiting bay and/or parking spot; ¶0075 one or more trained machine learning models may be trained using image data to learn vehicle identification (license plate), vehicle order association, vehicle routing, and/or vehicle tracking associated with image data of images of a drive-thru area)
(c) verifying parking area identification information of a parking area on which the vehicle parks when the server receives an image of the parked vehicle; (DeSantola ¶0030 outputs based on process of the image data can be assumed to assist in one or more drive-thru operations; the processed image data may be leveraged to identify vehicles in image(s), track vehicles across frames, associate pending meal orders with the vehicles, etc.; processed image frames may indicate relative locations of cameras disposed within a drive thru area and relative position of vehicles within the drive-thru area; ¶0049 drive-thru tracking system receives image data (e.g., frames of videos) from one or more cameras that are external to the restaurant and directed at a drive-thru, parking lot, surrounding streets, etc. and processes the image data to determine information about vehicles in the drive-thru; ¶0067 the vehicle identification tool, may identify vehicles by determining a visual indicator (e.g., license plate, make/model of the vehicle); vehicle identification tool may include a database of vehicles indicating an order history associated with each identified vehicle. The order association tool may associate a vehicle disposed within the drive-thru area with a pending meal order)
DeSantola in view of Lyles discloses:
(d) matching user’s individual information related to the parked vehicle with the parking area identification information, an entry time and a recognized license plate to complete an entry as a user terminal transmits phone number or information concerning means of payment inputted through a link in the QR code installed to the parking area to the server or the server verifies user’s identification information stored in association with the recognized license plate; and
DeSantola discloses completing entry by associating the parking area identification information, and recognized license plate with user profile and payment method received via a QR code link, or, when pre-registered, by automatically retrieving the user profile linked to the recognized license plate: (DeSantola ¶0237 display may depict elements of a current meal order including one or more meal items and associated prices including a total price and may include displaying data that identifies the order on an external endpoint device, e.g., the display may show a barcode (e.g., such as a quick response (QR) code) that may be registered by an endpoint device associated with a user account indicative of the customer and customer vehicle (e.g., using an app on a customer's phone); the vehicles may be tracked as they progress through the drive-thru; QR code associated with an order associated with a particular vehicle may be automatically displayed on the display; customer may then scan the QR code with their mobile device to pay for their order; see also ¶0308). DeSantola does not explicitly disclose completing entry by associating the parking area identification information, entry time, and recognized license plate with user profile including phone number and payment method received via a QR-code link, or, when pre-registered, by automatically retrieving the user profile linked to the recognized license plate. Lyles suggests or discloses this limitation/concept: (Lyles ¶0009 detecting a license plate number in a first image, captured by an entry camera unit arranged near an entrance of the parking structure at a first time; ¶0012 read license plate numbers of vehicles entering and exiting the parking structure; write the license plate number to a data log with the vehicle's time of entry (e.g., a timestamp of an image captured by the entry camera unit and depicting the license plate number); see also ¶0013; ¶0034 scans this set of timestamped images received from the entry camera unit for a license plate number of a vehicle; ¶0051 user may submit an application form in paper, via email, or online (e.g., within a web browser or native application) to add a license plate to the list; the user may: supply a license plate number; specify a particular parking structure, parking structure complex, parking structure network, or location (e.g., an office park, a city block); start and end dates; and/or contact information (e.g., an email address, a phone number) in the application). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify DeSantola to include completing entry by associating the parking area identification information, entry time, and recognized license plate with user profile including phone number and payment method received via a QR-code link, or, when pre-registered, by automatically retrieving the user profile linked to the recognized license plate as taught by Lyles. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify DeSantola in order to manage vehicle parking in vehicle parking systems (see ¶0002 of Lyles).
(e) processing an exit of the vehicle related to the user terminal and performing payment of parking fee based on the information concerning means of payment when an exit signal is received from the user terminal or a license plate of an exited vehicle in the image received from the camera matches with the license plate of the vehicle related to the user terminal.
DeSantola discloses tracking a vehicle through the vehicle exiting the location, but does not explicitly disclose processing an exit of the vehicle related to the user profile and performing payment of parking fee based on the payment method when an exit signal is received from a user terminal or a license plate of an exited vehicle in the image received from the camera matches with the license plate of the vehicle related to the user profile. Lyles suggests or discloses this limitation/concept: (Lyles ¶0012 capture images of entry and exit lanes in the parking structure; to read license plate numbers of vehicles entering and exiting the parking structure; and to monitor validation (e.g., payment) status of vehicles carrying these license plate numbers; ¶0014 the kiosk (or exit camera unit, local computer system, or remote computer system) detects the license plate number in an exit image captured by the exit camera unit; ¶0030 disclosing the entry and exit cameras extracting the license plate numbers and retuning them for further handling; see also ¶0044; ¶0116 detect license plate numbers in images captured by an exit camera unit installed in the parking structure and can store exit times of these license plate numbers; upon detecting a license plate number in an image captured by the exit camera unit, the system queries the data log for the entry time and status of the license plate number; calculate a time difference between the entry time and the exit time of the license plate number; implement a parking fee model to transform this time difference into a final parking cost for the license plate number; fee charged to the associated payment method for this parking period). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify DeSantola to include processing an exit of the vehicle related to the user profile and performing payment of parking fee based on the payment method when an exit signal is received from a user terminal or a license plate of an exited vehicle in the image received from the camera matches with the license plate of the vehicle related to the user profile as taught by Lyles. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify DeSantola in order to manage vehicle parking in vehicle parking systems (see ¶0002 of Lyles).
Claim 10: The method of claim 1, wherein the step (e) further includes: receiving additional identification information concerning current location from the user terminal, or receiving pre-stored location information or current GPS value from the user terminal and providing guidance information to parking location of the vehicle based on current location of the user terminal, in the exit of the vehicle. (DeSantola ¶0030 processed image data may be leveraged to identify vehicles in image(s), track vehicles across frames, associate pending meal orders with the vehicles, track proximity of vehicles to a meal payment zone and/or a meal delivery zones, etc.; processed image frames may indicate relative locations of cameras disposed within a drive thru area and relative position of vehicles within the drive-thru area; ¶0049 receiving image data (e.g., frames of videos) from one or more cameras that are external to the restaurant and directed at a drive-thru, parking lot, surrounding streets, etc.; ¶0211 disclosing processing logic detecting a customer leaving; see also ¶0272, ¶0278)
Claim 20 is directed to a server. Claim 20 recites limitations that are parallel in nature as those addressed above for claim 10, which is directed towards a method. Claim 20 is therefore rejected for the same reasons as set forth above for claim 10.
Claim(s) 2-6, 8, 9, 12-16, 18, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over DeSantola (2022/0292618) in view of Lyles (2022/0230538) further in view of An (2025/0014363).
Claim 2: DeSantola discloses: The method of claim 1, wherein the step (a) includes:
storing a plurality of training data; extracting an image region corresponding to the license plate in each of the training data; (DeSantola ¶0075 retrieve image data from the data store and generate outputs (e.g., action data, depth data, object data, vehicle identification data, vehicle order association data; ¶0067 the vehicle identification tool, may identify vehicles by determining a visual indicator (e.g., license plate, make/model of the vehicle))
DeSantola in view of Lyles in view of An discloses:
DeSantola discloses receiving image data of a license plate for a vehicle, but does not explicitly disclose identifying text of the license plate in the extracted image region; generating refined training data by adding defect images to the extracted image region; and training a preset machine learning model by inputting the refined training data and corresponding texts to the preset machine learning model such that the text is outputted when the refined training data is inputted to the preset machine learning model. An suggests or discloses this limitation/concept:
identifying text of the license plate in the extracted image region; generating refined training data by adding defect images to the extracted image region; and (An ¶0067 region of interest extractor may first detect the vehicle license plate area in the image including the vehicle, and cause the license plate identifier to recognize the license plate in the detected region; see also ¶0068; ¶0087 artificial image change may include one or more of an image brightness change, a contrast change, a blur change, and image tampering. Such an image variation may contribute to greatly increasing the number of training data when applying machine learning; ¶0088 Fig. 8C images in which tampering or distortion has been applied to the vehicle image; vehicle image of FIG. 8C may affect machine learning of a vehicle that is dirty due to dust or dirt, and the vehicle image of FIG. 8D may affect machine learning of a snow-covered vehicle)
training a preset machine learning model by inputting the refined training data and corresponding texts to the preset machine learning model such that the text is outputted when the refined training data is inputted to the preset machine learning model. (An ¶0078 disclosing texts of the vehicle license plates; the license plate identifier compares the license plates through machine learning of the image itself rather than OCR, the license plate identifier may determine that the license plate of FIG. 7A and the license plate of FIGS. 7B to 7D are different. In particular, using supervised learning, AI may be trained with the license plates of FIGS. 7B to 7D that are different from the license plate of FIG. 7A)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify DeSantola in view of Lyles to include identifying text of the license plate in the extracted image region; generating refined training data by adding defect images to the extracted image region; and training a preset machine learning model by inputting the refined training data and corresponding texts to the preset machine learning model such that the text is outputted when the refined training data is inputted to the preset machine learning model as taught by An. One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to modify DeSantola in view Lyles in order to recognize vehicle identity using machine learning, and determine whether to block entry or exit of the vehicle accordingly (see ¶0002 of An).
Claim 12 is directed to a server. Claim 12 recites limitations that are parallel in nature as those addressed above for claim 2, which is directed towards a method. Claim 12 is therefore rejected for the same reasons as set forth above for claim 2.
Claim 3: The method of claim 2,
wherein a defect image is in arbitrary shape and color, and wherein a size of the defect image is smaller than a size of the extracted image region and the defect image occludes regions of the license plate.
DeSantola discloses receiving image data of a license plate for a vehicle, but does not explicitly disclose wherein a defect image is in arbitrary shape and color, and wherein a size of the defect image is smaller than a size of the extracted image region and the defect image occludes regions of the license plate. An suggests or discloses this limitation/concept: (An ¶0068 region of interest may include bumper shapes; vehicle license plate is a portion where contamination is minimized even in poor driving environments (e.g., dusty roads, snowy/rainy weather, etc.); see also Fig. 5B (arbitrary shape); Figs. 7A-7C and ¶0040 disclosing the vehicle license plate that are different in text size; ¶0072 region of interest for machine learning may include of one channel image in the case of a black and white image, and three channel images (R/G/B) in the case of a color image). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify DeSantola in view of Lyles to include that a defect image is in arbitrary shape and color, and wherein a size of the defect image is smaller than a size of the extracted image region and the defect image occludes regions of the license plate as taught by An 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; one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 13 is directed to a server. Claim 13 recites limitations that are parallel in nature as those addressed above for claim 3, which is directed towards a method. Claim 13 is therefore rejected for the same reasons as set forth above for claim 3.
Claim 4: The method of claim 3,
wherein the defect image is in plural shapes and colors, and wherein multiple refined training data corresponding to the license plate are generated.
DeSantola discloses receiving image data of a license plate for a vehicle, but does not explicitly disclose that the defect image is in plural shapes and colors, and wherein multiple refined training data corresponding to the license plate are generated. An suggests or discloses this limitation/concept: (An ¶0011 disclosing an image scrambler to apply an artificial image change to at least one of the first region of interest and the second region of interest, and where the artificial image change includes one or more of an image brightness change, a contrast change, a blur change, and image tampering; ); Figs. 7A-7C and ¶0040 disclosing the vehicle license plate that are different in text size). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify DeSantola in view of Lyles to include that the defect image is in plural shapes and colors, and wherein multiple refined training data corresponding to the license plate are generated as taught by An 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; one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 14 is directed to a server. Claim 14 recites limitations that are parallel in nature as those addressed above for claim 4, which is directed towards a method. Claim 14 is therefore rejected for the same reasons as set forth above for claim 4.
Claim 5: The method of claim 3,
wherein the defect image is in a shape and a color for simulating snow or rain.
DeSantola discloses receiving image data of a license plate for a vehicle, but does not explicitly disclose that the defect image is in a shape and a color for simulating snow or rain. An suggests or discloses this limitation/concept: (An ¶0088 Fig. 8C images in which tampering or distortion has been applied to the vehicle image; vehicle image of FIG. 8C may affect machine learning of a vehicle that is dirty due to dust or dirt, and the vehicle image of FIG. 8D may affect machine learning of a snow-covered vehicle; ¶0068 vehicle license plate is a portion where contamination is minimized in poor driving environments (e.g., dusty roads, snowy/rainy weather, etc.)). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify DeSantola in view of Lyles to include that the defect image is in a shape and a color for simulating snow or rain as taught by An 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; one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 15 is directed to a server. Claim 15 recites limitations that are parallel in nature as those addressed above for claim 5, which is directed towards a method. Claim 15 is therefore rejected for the same reasons as set forth above for claim 5.
Claim 6: The method of claim 3,
wherein each of the defect images added in the extracted image region has different size at different positions.
DeSantola discloses receiving image data of a license plate for a vehicle, but does not explicitly disclose that each of the defect images added in the extracted image region has different size at different positions. An suggests or discloses this limitation/concept: (An Figs. 7A-7C and ¶0040 disclosing the vehicle license plate that are different in text size; ¶0081 FIG. 7A has the same font as the license plate 41c of FIG. 7C, but has a blank of a different size compared to the outline of the license plate). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify DeSantola in view of Lyles to include that the defect image is in a shape and a color for simulating snow or rain as taught by An 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; one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 16 is directed to a server. Claim 16 recites limitations that are parallel in nature as those addressed above for claim 6, which is directed towards a method. Claim 16 is therefore rejected for the same reasons as set forth above for claim 6.
Claim 8: The method of claim 1, wherein the step (b) includes the steps of:
(b-1) extracting a license plate region as a polygonal shape by recognizing a boundary of the license plate from the image captured by the camera; (b-2) transforming coordinates of pixels included in the license plate region such that the polygonal shape is transformed to preset rectangular shape; and (b-3) extracting the text information from the license plate region with transformed coordinates.
DeSantola discloses receiving image data of a license plate for a vehicle, but does not explicitly disclose (b-1) extracting a license plate region as a polygonal shape by recognizing a boundary of the license plate from the image captured by the camera; (b-2) transforming coordinates of pixels included in the license plate region such that the polygonal shape is transformed to preset rectangular shape; and (b-3) extracting the text information from the license plate region with transformed coordinates. An suggests or discloses this limitation/concept: (An ¶0068 FIG. 5A illustrates a full image 50 of a vehicle; FIG. 5B illustrates a region of interest 40 of the vehicle extracted from the full image 50 of FIG. 5A; FIG. 5A, the region of interest 40 is a partial image of the vehicle that includes at least a vehicle license plate area 41 of the full vehicle image 50. The criteria for setting such a region of interest 40 may vary, but the region of interest 40 may include bumper shapes, etc.; region of interest 40 may be obtained, for example, by applying a multiple to the up, down, left, and right sides based on a size of the vehicle license plate area 41; ¶0078 texts of the vehicle license plate in FIGS. 7A to 7D). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify DeSantola in view of Lyles to include (b-1) extracting a license plate region as a polygonal shape by recognizing a boundary of the license plate from the image captured by the camera; (b-2) transforming coordinates of pixels included in the license plate region such that the polygonal shape is transformed to preset rectangular shape; and (b-3) extracting the text information from the license plate region with transformed coordinates as taught by An 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; one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 18 is directed to a server. Claim 18 recites limitations that are parallel in nature as those addressed above for claim 8, which is directed towards a method. Claim 18 is therefore rejected for the same reasons as set forth above for claim 8.
Claim 9: The method of claim 8,
wherein the steps (b-1) to (b-3) are performed when the license plate region in the image captured by the camera is not in a predefined rectangular shape, or when the license plate region appears skewed due to the camera being positioned at an acute angle relative to the vehicle from above, below or sides.
DeSantola discloses receiving image data of a license plate for a vehicle, but does not explicitly disclose that the steps (b-1) to (b-3) are performed when the license plate region in the image captured by the camera is not in a predefined rectangular shape, or when the license plate region appears skewed due to the camera being positioned at an acute angle relative to the vehicle from above, below or sides. An suggests or discloses this limitation/concept: (An Fig. 6A-6B and ¶0069 FIG. 6A is the same vehicle image, but a capturing angle is different. Therefore, the extracted region of interest may also be skewed in a predetermined direction, as illustrated in FIG. 6B; the multiples applied to the up, down, left, and right sides are the same as in FIG. 5B, even the skewed region of interest 40′ as illustrated in FIG. 6B may correspond to the region of interest 40 illustrated in FIG. 5B in a one-to-one manner). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify DeSantola in view of Lyles to include that the steps (b-1) to (b-3) are performed when the license plate region in the image captured by the camera is not in a predefined rectangular shape, or when the license plate region appears skewed due to the camera being positioned at an acute angle relative to the vehicle from above, below or sides as taught by An 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; one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 19 is directed to a server. Claim 19 recites limitations that are parallel in nature as those addressed above for claim 9, which is directed towards a method. Claim 19 is therefore rejected for the same reasons as set forth above for claim 9.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over DeSantola (2022/0292618) in view of Lyles (2022/0230538) further in view of An (2025/0014363) further in view of Ni (2021/0209395).
Claim 7: The method of claim 3,
wherein the generating the refined training data includes: adding multiple Gaussian-blur masks or multiple white occlusion masks to arbitrary local regions within the extracted image region.
DeSantola discloses generating license plate data for training, but does not explicitly disclose generating the refined training data includes: adding multiple Gaussian-blur masks or multiple white occlusion masks to arbitrary local regions within the extracted image region. Ni suggests or discloses this limitation/concept: (Ni ¶0133 disclosing an average value of three color channels R. G and B in the real license plate image is counted. A brightness of the synthesized license plate image is adjusted based on a ratio of a maximum value of the average value to 255. A Gaussian blur is added to reduce image noise and detail level of the synthesized license plate image). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify DeSantola in view of Lyles further in view of An to include that the generating the refined training data includes: adding multiple Gaussian-blur masks or multiple white occlusion masks to arbitrary local regions within the extracted image region as taught by Ni 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; one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claim 17 is directed to a server. Claim 17 recites limitations that are parallel in nature as those addressed above for claim 7, which is directed towards a method. Claim 17 is therefore rejected for the same reasons as set forth above for claim 7.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DIONE N SIMPSON whose telephone number is (571)272-5513. The examiner can normally be reached M-F; 7:30 a.m.-4:30 p.m..
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DIONE N. SIMPSON
Primary Examiner
Art Unit 3628
/DIONE N. SIMPSON/Primary Examiner, Art Unit 3628