DETAILED ACTION
Notice of Pre-AIA or AIA Status
This is in response to application no. 18/901,516 filed on September 30, 2024. 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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 11 and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 11 recites “wherein the edge computing device is further configured to identify a primary violating vehicle which causes the leading violating vehicles to fail to yield to the ambulance.” There is insufficient antecedent basis for “the leading violating vehicles” (plural) in the claim.
Claim 18 recites “wherein the edge computing device is further configured to identify a primary violating vehicle which causes the leading violating vehicles to fail to yield to the ambulance.” There is insufficient antecedent basis for “the edge computing device” in the claim.
Claim Rejections - 35 USC § 102
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 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.
Claim(s) 1 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Seyfried et al. (US 20140169633 A1).
Regarding claim 1, Seyfried teaches a system for detection of a vehicle that blocks an ambulance while the ambulance is responding to an emergency call, comprising: a processor; and a camera communicatively connected to the processor, wherein the camera has a field of view that encompasses a surrounding of the ambulance (Figs. 1-7, ¶0025, 0072-0075: a front facing image capturing unit 506 with a field of view 540 and a rear facing image capturing unit 507 with a field of view 545. Field of view 540 and 545 …The system 120 includes a forward facing and a rear facing camera device 702, which are mounted on an emergency vehicle 704. The system also includes a processor 722…), wherein the processor is configured to identify a leading violating vehicle that blocks the ambulance while the ambulance is responding to an emergency call (abstract, ¶0060: the video is processed to identify any vehicles in violation within a prescribed distance from the emergency vehicle. ¶0072: FIGS. [5], 6 and 7, illustrated is one emergency vehicle response scenario which includes two vehicles 510 and 520 failing to yield to an emergency vehicle 505 within a 500 foot prescribed distance…).
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.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seyfried et al. (US 20140169633 A1) in view of Kim et al. (US 20210207971 A1).
Regarding claim 2, Seyfried teaches wherein the processor is an edge computing device comprising: a graphical processing unit (GPU) (¶0075-0076: a processor 722 which is configured to process image data). Seyfried at ¶0039 further discloses details recorded of the violations include a GPS information.
Seyfried does not explicitly disclose a global positioning system (GPS) communicatively connected to the GPU; a communication unit communicatively connected to the GPU; a power management unit connected to the GPU; a supervisory unit communicatively connected to the GPU and the power management unit; and a vehicle input unit communicatively connected to the supervisory unit, wherein the vehicle input unit is configured to receive a plurality of input values from an operator.
However, Kim teaches a global positioning system (GPS) communicatively connected to the GPU (Figs. 1 and 3, ¶0086: an electronic device (e.g., the electronic device 101 in FIG. 1) according to various embodiments may include a function processing module 300 … The function processing module 300 may include at least one of a first information collection module 310. ¶0093-0094, 0170: first information collection module 310 may include at least one of a GPS sensor); a communication unit communicatively connected to the GPU (Fig. 1: a communication module 190, auxiliary processor 123 (e.g., a graphics processing unit (GPU)); a power management unit connected to the GPU (Fig. 1: a power management module 188, auxiliary processor 123 (e.g., a graphics processing unit (GPU)); a supervisory unit communicatively connected to the GPU and the power management unit (Fig. 1: a main processor 121 (e.g., a central processing unit (CPU) , an auxiliary processor 123 (e.g., a graphics processing unit (GPU), a power management module 188); and a vehicle input unit communicatively connected to the supervisory unit (Fig. 1: input device 150, a main processor 121 (e.g., a central processing unit (CPU)), wherein the vehicle input unit is configured to receive a plurality of input values from an operator (¶0054: the input device 150 may receive a command or data to be used by another component (e.g., the processor 120) of the electronic device 101, from the outside (e.g., a user) of the electronic device 101).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Seyfried’s vehicle video based violation enforcement method and system by incorporating the electronic device comprising interconnected components as taught by Kim, since such a modification would have been a predictable use of prior art elements according to their established function to allow coordinated processing in a computing device.
Claim(s) 3-7 and 9-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seyfried et al. (US 20140169633 A1) in view of Kim et al. (US 20210207971 A1) as applied to claim 2, and further in view of SANGHOON ( KR20230102871A).
Regarding claim 3, Seyfried teaches wherein the edge computing device is configured to execute a program instruction comprising: processing a video captured from the camera to obtain a plurality of video frames (Figs. 1-5: ¶0025, 0055-0060: the system receives a video stream from a forward facing image capturing device mounted to an emergency vehicle…the system processes the video stream to identify two or more video frames including a detected vehicle. ¶0061-0063: the system receives a video stream from a rear facing image capturing device mounted to an emergency vehicle…the system processes the video stream to identify two or more video frames including a detected vehicle within a prescribed distance in a rear of the emergency vehicle); detecting a plurality of vehicles in the plurality of video frames (¶0055-0060: the system receives a video stream from a forward facing image capturing device mounted to an emergency vehicle…the system processes the identified two or more video frames to determine if the detected vehicle is yielding to the emergency vehicle. ¶0062-0064: the system receives a video stream from a rear facing image capturing device mounted to an emergency vehicle…the system processes the identified two or more video frames to determine if the detected vehicle is following the emergency vehicle. 0072: FIGS. [5], 6 and 7, illustrated is one emergency vehicle response scenario which includes two vehicles 510 and 520 failing to yield to an emergency vehicle 505 within a 500 foot prescribed distance and a third vehicle 515 which is following the emergency vehicle 505 too close, i.e. within the prescribed distance of 200 feet. ); detecting and maintaining a vehicle trajectory of each vehicle of the plurality of vehicles (¶0053: the system processes the identified two or more video frames to determine if the detected vehicle is yielding to the emergency vehicle according to a prescribed law…¶0072: FIGS. [5], 6 and 7, illustrated is one emergency vehicle response scenario which includes two vehicles 510 and 520 failing to yield to an emergency vehicle 505 within a 500 foot prescribed distance and a third vehicle 515 which is following the emergency vehicle 505 too close, i.e. within the prescribed distance of 200 feet. ¶0076: the processor identifies video frames that show a vehicle illegally failing to yield to the emergency vehicle and/or following the emergency vehicle too close. The tagged frames 738 are stored in the memory) Note that the term “maintaining” in this limitation is interpreted as storing in the memory the identified video frames containing the leading/following vehicles image within the prescribed distance of the emergency vehicle; determining a presence of the leading violating vehicle based on the vehicle trajectory of each vehicle of the plurality of vehicles (¶0060: the system processes the identified two or more video frames to determine if the detected vehicle is yielding to the emergency vehicle according to a prescribed law…¶0072: FIGS. [5], 6 and 7, illustrated is one emergency vehicle response scenario which includes two vehicles 510 and 520 failing to yield to an emergency vehicle 505 within a 500 foot prescribed distance and a third vehicle 515 which is following the emergency vehicle 505 too close, i.e. within the prescribed distance of 200 feet); identifying a license plate number of the leading violating vehicle (¶0055-0060: the system receives a video stream from a forward facing image capturing device mounted to an emergency vehicle… the system determines if the detected vehicle within the video frames includes a completely visible license plate border); and storing the license plate number of the leading violating vehicle and the plurality of video frames in a database (¶0075-0076: Using the license plate analysis module, the processor identifies video frames that show a vehicle illegally failing to yield to the emergency vehicle and/or following the emergency vehicle too close. The tagged frames 738 are stored in the memory, and the processor additionally tags and stores tagged video segments 740 that include the tagged frames).
Seyfried in view of Kim does not explicitly disclose assigning an identification number to each vehicle of the plurality of vehicles.
However, SANGHOON teaches assigning an identification number to each vehicle of the plurality of vehicles (Pages 9, 13: multi-object tracking step of assigning IDs to vehicle objects detected in consecutive video frames. 10A to 10C are configuration diagrams showing results of multiple object tracking).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Seyfried’s vehicle video based violation enforcement method and system by incorporating the teaching of a multi-object tracking system as taught by SANGHOON, in order to increase the accuracy of the vehicle detection and tracking (SANGHOON: page 3).
Regarding claim 4, Seyfried in view of Kim and SANGHOON teaches the system of claim 3. SANGHOON further teaches wherein the detecting the plurality of vehicles is performed with a deep neural network (abstract, pages 8-9: DeepSORT Multi-object tracking; multi-object tracking step is performed by learning the YOLOv5 model with the collected learning data… detecting the vehicle object to classify the vehicle type Vehicle object detection and a vehicle object tracking step of assigning an ID to a vehicle object detected in consecutive video frames…). The motivation statement set forth above with respect to claim 3 applies here.
Regarding claim 5, Seyfried in view of Kim and SANGHOON teaches the system of claim 4. SANGHOON further teaches wherein the deep neural network is YOLO (abstract: YOLO). The motivation statement set forth above with respect to claim 3 applies here.
Regarding claim 6, Seyfried in view of Kim and SANGHOON teaches the system of claim 3. SANGHOON further teaches wherein the assigning is performed with a tracking neural network (Pages 8-9: by learning the YOLOv5 model with the collected learning data, identifying the location of the vehicle object in the image through the multi-object tracking algorithm, and detecting the vehicle object to classify the vehicle type Vehicle object detection and a vehicle object tracking step of assigning an ID to a vehicle object detected in consecutive video frames). The motivation statement set forth above with respect to claim 3 applies here.
Regarding claim 7, Seyfried in view of Kim and SANGHOON teaches the system of claim. SANGHOON further teaches wherein the tracking neural network is Deep SORT (abstract: DeepSORT). The motivation statement set forth above with respect to claim 3 applies here.
Regarding claim 9, Seyfried teaches wherein the edge computing device is further configured to identify a primary violating vehicle which causes the leading violating vehicle to fail to yield to the ambulance (¶0060: the system processes the identified two or more video frames to determine if the detected vehicle is yielding to the emergency vehicle according to a prescribed law…¶0072: FIGS. [5], 6 and 7, illustrated is one emergency vehicle response scenario which includes two vehicles 510 and 520 failing to yield to an emergency vehicle 505 within a 500 foot prescribed distance…).
Regarding claim 10, Seyfried teaches wherein the camera is disposed on a top of the ambulance, wherein the camera is configured to capture an aerial view around the ambulance (¶0025: forward and rear facing camera systems mounted on an emergency vehicle. ¶0073: a front facing image capturing unit 506 with a field of view 540 and a rear facing image capturing unit 507 with a field of view 545). Note that a camera mounted on the top of a vehicle is well-known in the art.
Regarding claim 11, Seyfried teaches wherein the edge computing device is further configured to identify a primary violating vehicle which causes the leading violating vehicles to fail to yield to the ambulance (¶0060: the system processes the identified two or more video frames to determine if the detected vehicle is yielding to the emergency vehicle according to a prescribed law…¶0072: FIGS. [5], 6 and 7, illustrated is one emergency vehicle response scenario which includes two vehicles 510 and 520 failing to yield to an emergency vehicle 505 within a 500 foot prescribed distance…).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seyfried et al. (US 20140169633 A1) in view of in view of Kim et al. (US 20210207971 A1) and SANGHOON ( KR20230102871A) as applied to claim 3, and further in view of Silver et al. (US 9558659 B1).
Regarding claim 8, Seyfried in view of Kim and SANGHOON does not teach the determining the presence of the leading violating vehicle further comprises: determining whether a first vehicle of the plurality of vehicles is blocking the ambulance for a predetermined duration based on the plurality of input values; and identifying the first vehicle as the leading violating vehicle.
However, Silver teaches the determining the presence of the leading violating vehicle further comprises: determining whether a first vehicle of the plurality of vehicles is blocking the ambulance for a predetermined duration based on the plurality of input values (col. 2, lines 6-8:the method includes detecting, by the one or more computing devices, that the first vehicle has remained stationary for a predetermined period of time. Col. 10, lines 27-45: autonomous vehicle 520 may continue to designate vehicle 520 as being in a short-term stationary state, even if vehicle 520 does not begin to move for a predetermined time period after traffic light 536 has turned green); and identifying the first vehicle as the leading violating vehicle (Col. 10, lines 27-45: autonomous vehicle 100 may identify a vehicle at the position of vehicle 552 as blocking vehicle 520 from legally proceeding into intersection 502).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Seyfried’s vehicle video based violation enforcement method and system by incorporating the teaching of Silver as noted above, in order to identify vehicles blocking the movement of the vehicle (Silver: col. 10, lines 27-45).
Claim(s) 12 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seyfried et al. (US 20140169633 A1) in view of in view of Kim et al. (US 20210207971 A1) and SANGHOON ( KR20230102871A) as applied to claim 3, and further in view of Yerakaraju et al. (US 20250131738 A1).
Regarding claims 12 and 13, Seyfried teaches wherein the communication unit of the edge computing device…configured to continuously detect the leading violating vehicle or the primary violating vehicle (abstract: the video is processed to identify any vehicles in violation within a prescribed distance from the emergency vehicle. ¶0072: FIGS. [5], 6 and 7, illustrated is one emergency vehicle response scenario which includes two vehicles 510 and 520 failing to yield to an emergency vehicle 505 within a 500 foot prescribed distance…).
Seyfried in view of Kim and SANGHOON does not teach wherein the communication unit of the edge computing device is connected to a cloud system comprising an artificial intelligence engine, wherein the artificial intelligence engine is configured to continuously detect the leading violating vehicle or the primary violating vehicle, wherein the Artificial intelligence engine is based on a Random Forest.
Yerakaraju disclose an object detection system from images that are captured by cameras attached to a car. The captured images may be transmitted to a cloud computing system 115. The cloud computing system 115 can be or include one or more computing devices that are configured to train and distribute machine learning models for object detection from images and/or similar tasks relating to real-time operation of a vehicle. The cloud computing system 115 may train machine learning models 160 with the decoded images. The machine learning models 160 may be neural networks, support vector machines, and/or random forests. Figs. 1-3, ¶0041-0042, 0048.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Seyfried’s vehicle video based violation enforcement method and system by incorporating the teaching of Yerakaraju to arrive at the claimed invention of “wherein the communication unit of the edge computing device is connected to a cloud system comprising an artificial intelligence engine, wherein the artificial intelligence engine is configured to continuously detect the leading violating vehicle or the primary violating vehicle, wherein the Artificial intelligence engine is based on a Random Forest,” as recited in claims 12-13, in order to obtain a system which accurately detects objects from images in real-time (Yerakaraju: ¶005-0006).
Claim(s) 14-16 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seyfried et al. (US 20140169633 A1) in view of SANGHOON ( KR20230102871A).
Regarding claim 14, Seyfried teaches a method of detecting a leading violating vehicle that blocks an ambulance while the ambulance is responding to an emergency call, comprising: processing a video captured from a plurality of cameras to obtain a plurality of video frames, wherein the plurality of cameras is mounted on the ambulance and configured to capture the video of a surrounding of the ambulance (Figs. 1-5: ¶0025, 0055-0060: the system receives a video stream from a forward facing image capturing device mounted to an emergency vehicle…the system processes the video stream to identify two or more video frames including a detected vehicle. ¶0061-0063: the system receives a video stream from a rear facing image capturing device mounted to an emergency vehicle…the system processes the video stream to identify two or more video frames including a detected vehicle within a prescribed distance in a rear of the emergency vehicle); detecting a plurality of vehicles in the plurality of video frames (¶0055-0060: the system receives a video stream from a forward facing image capturing device mounted to an emergency vehicle…the system processes the identified two or more video frames to determine if the detected vehicle is yielding to the emergency vehicle. ¶0062-0064: the system receives a video stream from a rear facing image capturing device mounted to an emergency vehicle…the system processes the identified two or more video frames to determine if the detected vehicle is following the emergency vehicle. ¶0072: FIGS. [5], 6 and 7, illustrated is one emergency vehicle response scenario which includes two vehicles 510 and 520 failing to yield to an emergency vehicle 505 within a 500 foot prescribed distance…); detecting and maintaining a vehicle trajectory of each vehicle of the plurality of vehicles (¶0052: the system processes the video stream to identify two or more video frames including a detected vehicle within a prescribed distance in a front of the emergency vehicle.¶0063: the system processes the video stream to identify two or more video frames including a detected vehicle within a prescribed distance in a rear of the emergency vehicle. ¶0072: FIGS. [5], 6 and 7, illustrated is one emergency vehicle response scenario which includes two vehicles 510 and 520 failing to yield to an emergency vehicle 505 within a 500 foot prescribed distance and a third vehicle 515 which is following the emergency vehicle 505 too close, i.e. within the prescribed distance of 200 feet. ¶0076: the processor identifies video frames that show a vehicle illegally failing to yield to the emergency vehicle and/or following the emergency vehicle too close. The tagged frames 738 are stored in the memory). Note that the term “maintaining” in this limitation is interpreted as storing in the memory the identified video frames containing the leading/following vehicles image within the prescribed distance of the emergency vehicle; determining a presence of the leading violating vehicle based on the vehicle trajectory (¶0060: the system processes the identified two or more video frames to determine if the detected vehicle is yielding to the emergency vehicle according to a prescribed law…¶0072: FIGS. [5], 6 and 7, illustrated is one emergency vehicle response scenario which includes two vehicles 510 and 520 failing to yield to an emergency vehicle 505 within a 500 foot prescribed distance…); identifying a license plate number of the leading violating vehicle (¶0076-0077: Using the license plate analysis module, the processor identifies video frames that show a vehicle illegally failing to yield to the emergency vehicle and/or following the emergency vehicle too close…the processor executes an automated license plate recognition (ALPR) module 744 that identifies the license plate number of vehicles and the state of origin); and storing the license plate number of the leading violating vehicle and the plurality of video frames in a database (¶0075-0076: Using the license plate analysis module, the processor identifies video frames that show a vehicle illegally failing to yield to the emergency vehicle and/or following the emergency vehicle too close. The tagged frames 738 are stored in the memory, and the processor additionally tags and stores tagged video segments 740 that include the tagged frames).
Seyfried does not explicitly disclose assigning an identification number to each vehicle of the plurality of vehicles.
However, SANGHOON teaches assigning an identification number to each vehicle of the plurality of vehicles (Pages 9, 13: multi-object tracking step of assigning IDs to vehicle objects detected in consecutive video frames. 10A to 10C are configuration diagrams showing results of multiple object tracking).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Seyfried’s vehicle video based violation enforcement method and system by incorporating the teaching of multi-object tracking system as taught by SANGHOON, in order to increase the accuracy of the vehicle detection and tracking (SANGHOON: page 3).
Regarding claim 15, Seyfried in view of SANGHOON teaches the method of claim 14. SANGHOON further teaches wherein the detecting the plurality of vehicles is performed with a deep neural network and wherein the assigning is performed with a tracking neural network (abstract, page 3: YOLOv5 model learning with the collected learning data, and detects vehicle objects through multi-object tracking algorithm through DeepSORT and a multi-object tracking unit performing tracking. Pages 8-9: by learning the YOLOv5 model with the collected learning data, identifying the location of the vehicle object in the image through the multi-object tracking algorithm, and detecting the vehicle object to classify the vehicle type Vehicle object detection and a vehicle object tracking step of assigning an ID to a vehicle object detected in consecutive video frames). The motivation statement set forth above with respect to claim 15 applies here.
Regarding claim 16, Seyfried in view of SANGHOON teaches the method of claim 14. SANGHOON further teaches wherein the deep neural network is YOLO and wherein the tracking neural network is Deep SORT (abstract: YOLO, DeepSORT). The motivation statement set forth above with respect to claim 15 applies here.
Regarding claim 19, Seyfried teaches wherein the camera is disposed on a top of the ambulance, wherein the camera is configured to capture an aerial view around the ambulance (¶0025: forward and rear facing camera systems mounted on an emergency vehicle. ¶0073: a front facing image capturing unit 506 with a field of view 540 and a rear facing image capturing unit 507 with a field of view 545). Note that a camera mounted on the top of a vehicle is well-known in the art.
Claim(s) 17 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seyfried et al. (US 20140169633 A1) in view of SANGHOON ( KR20230102871A) as applied to claim 14, and further in view of Silver et al. (US 9558659 B1).
Regarding claim 17, Seyfried in view of SANGHOON does not teach determining whether a first vehicle of the plurality of vehicles is blocking the ambulance for a predetermined duration based on the plurality of input values; and identifying the first vehicle as the leading violating vehicle.
However, Silver teaches determining whether a first vehicle of the plurality of vehicles is blocking the ambulance for a predetermined duration based on the plurality of input values; and identifying the first vehicle as the leading violating vehicle (col. 2, lines 6-8:the method includes detecting, by the one or more computing devices, that the first vehicle has remained stationary for a predetermined period of time. Col. 10, lines 27-45: autonomous vehicle 520 may continue to designate vehicle 520 as being in a short-term stationary state, even if vehicle 520 does not begin to move for a predetermined time period after traffic light 536 has turned green); and identifying the first vehicle as the leading violating vehicle (Col. 10, lines 27-45: autonomous vehicle 100 may identify a vehicle at the position of vehicle 552 as blocking vehicle 520 from legally proceeding into intersection 502).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Seyfried’s vehicle video based violation enforcement method and system by incorporating the teaching of Silver as noted above, in order to identify vehicles blocking the movement of the vehicle (Silver: col. 10, lines 27-45).
Regarding claim 18, Seyfried teaches wherein the edge computing device is further configured to identify a primary violating vehicle which causes the leading violating vehicles to fail to yield to the ambulance (¶0060: the system processes the identified two or more video frames to determine if the detected vehicle is yielding to the emergency vehicle according to a prescribed law…¶0072: FIGS. [5], 6 and 7, illustrated is one emergency vehicle response scenario which includes two vehicles 510 and 520 failing to yield to an emergency vehicle 505 within a 500 foot prescribed distance…).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Seyfried et al. (US 20140169633 A1) in view of SANGHOON ( KR20230102871A) as applied to claim 14, and further in view of Yerakaraju et al. (US 20250131738 A1).
Regarding claim 20, Seyfried teaches wherein the edge computing device…configured to continuously detect the leading violating vehicle or the primary violating vehicle (¶0060: the system processes the identified two or more video frames to determine if the detected vehicle is yielding to the emergency vehicle according to a prescribed law…¶0072: FIGS. [5], 6 and 7, illustrated is one emergency vehicle response scenario which includes two vehicles 510 and 520 failing to yield to an emergency vehicle 505 within a 500 foot prescribed distance…).
Seyfried in view of Kim and SANGHOON does not teach wherein the edge computing device is connected to a cloud system comprising an artificial intelligence engine based on a Random Forest, and wherein the artificial intelligence engine based on the Random Forest is configured to continuously detect the leading violating vehicle or the primary violating vehicle.
Yerakaraju disclose an object detection system from images that are captured by cameras attached to a car. The captured images may be transmitted to a cloud computing system 115. The cloud computing system 115 can be or include one or more computing devices that are configured to train and distribute machine learning models for object detection from images and/or similar tasks relating to real-time operation of a vehicle. The cloud computing system 115 may train machine learning models 160 with the decoded images. The machine learning models 160 may be neural networks, support vector machines, and/or random forests. Figs. 1-3, ¶0041-0042, 0048.
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify Seyfried’s vehicle video based violation enforcement method and system by incorporating the teaching of Yerakaraju to arrive at the claimed invention of “wherein the edge computing device is connected to a cloud system comprising an artificial intelligence engine based on a Random Forest, and wherein the artificial intelligence engine based on the Random Forest is configured to continuously detect the leading violating vehicle or the primary violating vehicle,” as recited in claim 20, in order to obtain a system which accurately detects objects from images in real-time (Yerakaraju: ¶0005-0006).
The following are the prior art made of record and not relied upon are considered pertinent to applicant's disclosure.
Bachelder et al. (US 20050116838 A1) describes “Detection And Enforcement Of Failure-to-yield In An Emergency Vehicle Preemption System” Title
WANG et al. (US 20190370574 A1) describes” an object detection framework that uses bounding boxes to define the detection of an object in the image input” ¶0050.
Zuraimi et al. describes "Vehicle Detection and Tracking using YOLO and DeepSORT".
Conclusion
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/NATHNAEL AYNALEM/Primary Examiner, Art Unit 2488