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 .
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.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) a mental process which the human mind can perform an observation, evaluation and judgement. This judicial exception is not integrated into a practical application because the claims are directed to mental processes without any significantly more. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a human can organize and perform the mental process. Below is the analysis.
Claim 1 recites, “A system comprising: a first mobile unit configured to transport a mobile surveillance system, said surveillance system comprising: one or more cameras mounted on the mobile unit, said cameras configured to capture images of a second mobile unit; at least one license plate reader operatively coupled to the one or more cameras; and a processing unit configured to analyze captured data for detecting traffic violations associated with the second mobile unit.”
Step 2A Prong One: the claim is an abstract idea of nature or natural phenomenon because of the recited step of, “one or more cameras mounted on the mobile unit, said cameras configured to capture images of a second mobile unit; at least one license plate reader operatively coupled to the one or more cameras; and a processing unit configured to analyze captured data for detecting traffic violations associated with the second mobile unit” are simply generic structures capable of collecting data without offering any technical steps or innovative steps as to how analysis is conducted with the conclusions on determined violations respectively.
The claim recites “a processing unit configured to analyze captured data for detecting traffic violations associated with the second mobile unit”. Here, the claim fails to offer how a specific technical problem of how the generic processing unit analyzes the violation associated with a second mobile unit based on capture data. The analysis step is simply the gathering of data to determine a violation has been arrive by another mobile unit, step that may be arrived in the human mind.
Step 2A Prong Two: the claimed invention remains an abstract idea because The two “non-abstract” idea elements do not tie the abstract idea to anything substantially, i.e. cameras, license plate reader or processing unit fail to provide anything significantly from a technical innovative standpoint to elaborate on how the analysis is conducted as well as how the traffic violations related to the second mobile unit are concluded. Thereby, the invention is simply generic data collecting structures that are capable of collecting traffic data presentable for analysis to which the traffic violations may be conclusively arrived. Data that may be collected an gathered and be presented to be analyzed and concluded naturally by the human mind.
Claim 2 recites “wherein the one or more cameras are configured to capture images in a predetermined field of view.” Again, more structural components are described/established, without further establishing their operative/innovative purpose, as well as fail to link the innovative significance of how the analysis and the determined conclusion of traffic violations are met by the processing unit. Thereby, the claim does not offer anything significantly more to cure the invention of natural phenomena, which recites data that may be collected an gathered and be presented to be analyzed and concluded naturally by the human mind.
Claim 3 recites “further comprising a motorized scanning mechanism configured to adjust the orientation of the one or more cameras.” Again, more structural components are described/established, without further establishing their operative/innovative purpose, as well as fail to link the innovative significance of how the analysis and the determined conclusion of traffic violations are met by the processing unit. Thereby, the claim does not offer anything significantly more to cure the invention of natural phenomena, which recites data that may be collected an gathered and be presented to be analyzed and concluded naturally by the human mind.
Claim 4 recites “further comprising a control unit operably connected to the motorized scanning mechanism, the control unit configured to automatically direct the scanning mechanism to capture images at various angles.” Again, more structural components are described/established, without further establishing their operative/innovative purpose, as well as fail to link the innovative significance of how the analysis and the determined conclusion of traffic violations are met by the processing unit. Thereby, the claim does not offer anything significantly more to cure the invention of natural phenomena, which recites data that may be collected an gathered and be presented to be analyzed and concluded naturally by the human mind.
Claim 5 recites “wherein the control unit is further configured to automate scanning based on detected motion of surrounding vehicles.” Again, more structural components are described/established, without further establishing their operative/innovative purpose, as well as fail to link the innovative significance of how the analysis and the determined conclusion of traffic violations are met by the processing unit. Thereby, the claim does not offer anything significantly more to cure the invention of natural phenomena, which recites data that may be collected an gathered and be presented to be analyzed and concluded naturally by the human mind.
Claim 6 recites “processing unit is configured to, in response to a user input, override the automated scanning function for targeted image capture.” Again, more structural components are described/established, without further establishing their operative/innovative purpose, as well as fail to link the innovative significance of how the analysis and the determined conclusion of traffic violations are met by the processing unit. Thereby, the claim does not offer anything significantly more to cure the invention of natural phenomena, which recites data that may be collected an gathered and be presented to be analyzed and concluded naturally by the human mind.
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.
Claim(s) 1-3 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Subramanya (US 20140210646 A1).
In regards to claim 1, Subramanya teaches a system comprising a first mobile unit configured to transport a mobile surveillance system, said surveillance system comprising one or more cameras mounted on the mobile unit (Paragraphs 168, 169)
In various embodiments, one or more surveillance cameras can be mounted on vehicles and can perform vehicle audits, stolen vehicle discovery, parking violation enforcement, and other functions as required.[P-168]
FIG. 22 depicts a surveillance camera 2200 mounted on a vehicle 2202 in accordance with an exemplary embodiment. The vehicle 2202 may have a plurality of cameras mounted thereon to support vehicle enforcement operations, such as parking enforcement. The surveillance camera 2200 can use information provided by the roadside sensor network and navigation information from one or more sources to determine when to capture an image and from which camera. The surveillance camera 2200 can interface with the various sensors and systems described herein.[P-169]
Subramanya further teaches the said cameras configured to capture images of a second mobile unit, at least one license plate reader operatively coupled to the one or more cameras, and a processing unit configured to analyze captured data for detecting traffic violations associated with the second mobile unit (Paragraphs 174, 175, 183).
The surveillance camera also can be mounted on parking enforcement vehicles. The surveillance camera vehicles or operators can be routed to most efficiently capture violations using automated routing algorithms using including but not limited to genetic algorithms, neural network, point-to-multipoint, multipoint-to-multipoint routing algorithms and similar, using both current and future violation predictions based on a probabilistic models, real roadway or line of sight distances, and taking into account real time and/or historical travel times and prediction models of future travel times.[P-174]
Exemplary embodiments may include the ability to pay within a certain amount of time after the parking period. In a meterless parking situation, where some patrons, for example, such as visitors, may not have preset accounts, payment mechanisms, or registration in a given city, these patrons may be able to make a payment after the fact within a given time period through any of the payment mechanism such as city designated centers, online portals with the city or a third party service provider, setup an account with a mobile payment service, etc. In this scenario, vehicles may be flagged as violation or potential violations, but no notice is issued for a designated time period, say for example, 10 days, and the patron can up to a week (for example) to make payment, identifying the vehicle using license plate number, time of day, and/or parking space or block number. If payment is made for the vehicle within the predetermined time period, the notice is removed from issuance and no or reduced penalties are applied.[P-175]
FIG. 23 depicts a process flow 2300 for a surveillance camera subsystem in accordance with an exemplary embodiment. When a wake up signal is triggered at 2304 based on the location, timing, or other information from the main controller 2302, the subsystem is woken up in a fast manner, for example, in under 100 ms, and one or more snapshots 2306 are taken from the connected camera(s). For example, the connected cameras may include a license plate and/or environment camera. The image formats may include JPG, BMP, and YUV. Other image formats may be possible. The camera(s) may have any type of resolution necessary to capture the appropriate detail. For example, 3 megapixels may be used. The resulting images may be input to the subsystem processing at 2308 and are first processed through a preprocessor module at 2310 to first determine the region of the image that contains a potential license plate at 2312. The plate region may then used to decipher whether the plate contains multiple characters at 2314 and send the extracted characters at 2316 to the backend server and optionally an in-vehicle display. The entire image and/or the license plate region and/or the extracted text may be stored locally at 2318 for a programmed length of time or discarded based on business rules.[P-183]
In regards to claim 2, Subramanya teaches the one or more cameras are configured to capture images in a predetermined field of view (Paragraphs 51,162, 163)
FIG. 3 depicts an example parking space geometry 300 with the occupancy and vehicle identification sensors 303 integrated with a parking meter 301 and mounted on a pole 302. Vehicle occupancy sensing beams 305 with fields of view designed to encompass the zone of interest and a separate vehicle identification field of view 304 designed to target an in-vehicle device are shown.[P-53]
In various embodiments, the surveillance camera can have a disturbance and theft sensor that may report disturbance signal and/or current locations using its wireless communication means. The surveillance camera can have day and night modes that are switched based on an ambient light sensor or based on programmable time of day settings. The surveillance camera can be configured to record any combination of still images and video recordings suitable for the application and the cameras are oriented to capture all relevant details of the scene, such as traffic light or adjacent lanes, etc. In various embodiments, the focal length and aperture can be varied either manually or electrically or remotely adjusted in the field to optimize camera views.[P-162]
The optical field disturbance sensor can be based on one or more linear complementary metal-oxide-semiconductor (CMOS) or other photo cell arrays or similar where the rate of change of light intensity in one or more pixels of the sensors are used to determine whether a vehicle or object is in the field of view. One or more individual photo cells with or without optical filters and lens optics can be used as a disturbance sensor. The determination of the object in the field of view can be based on absolute change in light captured by the photo cell or cells or relative change and timing of change between cells. This may help differentiate between changes due to clouds, sunlight, rain, etc., and natural changes vs. an automobile moving in a specific direction. Direction of travel of the automobile also can be determined using this method.[P-163]
Here we see the configured camera(s) set to capture a predetermined field of view based on their configuration, including being mounted on parking meters or mobile vehicles alike.
In regards to claim 3, Subramanya teaches a motorized scanning mechanism configured to adjust the orientation of the one or more cameras (Paragraph 162, 164)
In various embodiments, the surveillance camera can have a disturbance and theft sensor that may report disturbance signal and/or current locations using its wireless communication means. The surveillance camera can have day and night modes that are switched based on an ambient light sensor or based on programmable time of day settings. The surveillance camera can be configured to record any combination of still images and video recordings suitable for the application and the cameras are oriented to capture all relevant details of the scene, such as traffic light or adjacent lanes, etc. In various embodiments, the focal length and aperture can be varied either manually or electrically or remotely adjusted in the field to optimize camera views.[P-162]
In various embodiments, infrared, Doppler, or thermal sensors can be used instead of or in combination with the broad spectrum radar or optical field disturbance sensor. Infrared allowance and/or cutoff filters using manual or electronic switching means can be used selectively for cameras based on day or night modes or whether the image being taken in a license plate image or a scene image. In various embodiments, image sensors with adjustable resolution, binning, and crop are used either in the imager chip or in the processing software to achieve optimal signal to noise, resolutions, and image sizes.[P-164]
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Subramanya (US 20140210646 A1)
In regards to claim 4, though Subramanya does not explicitly describe a control unit operably connected to the motorized scanning mechanism, the control unit configured to automatically direct the scanning mechanism to capture images at various angles.
By Subramanya teaching the optical sensors and cameras being configured automatically to adjust their respective field of view aperture or configuration (Paragraph 162, 164)
In various embodiments, the surveillance camera can have a disturbance and theft sensor that may report disturbance signal and/or current locations using its wireless communication means. The surveillance camera can have day and night modes that are switched based on an ambient light sensor or based on programmable time of day settings. The surveillance camera can be configured to record any combination of still images and video recordings suitable for the application and the cameras are oriented to capture all relevant details of the scene, such as traffic light or adjacent lanes, etc. In various embodiments, the focal length and aperture can be varied either manually or electrically or remotely adjusted in the field to optimize camera views.[P-162]
In various embodiments, infrared, Doppler, or thermal sensors can be used instead of or in combination with the broad spectrum radar or optical field disturbance sensor. Infrared allowance and/or cutoff filters using manual or electronic switching means can be used selectively for cameras based on day or night modes or whether the image being taken in a license plate image or a scene image. In various embodiments, image sensors with adjustable resolution, binning, and crop are used either in the imager chip or in the processing software to achieve optimal signal to noise, resolutions, and image sizes.[P-164]
As well as teaching the cameras being able to be configured to capture images from different angles (Paragraph 43)
A well-managed parking system requires accurate unique vehicle identification for vehicle based parking access and rate determination, motorist guidance, violation detection, and enforcement automation support. The disclosed embodiments enable advanced parking management features in a meter-less configuration, thereby potentially avoiding a large portion of capital and operating expenses to cities (in parking meters and the like). The disclosed embodiments make it possible to accurately and uniquely identify stationery or moving vehicles from very low power infrastructure components and provide on-street dynamic signage and guidance to motorists, take camera images from multiple angles to provide secondary revenue collection as well as enforcement evidence, and automation of booting processes for violator vehicles.[P-43]
With Subramanya’s camera disclosure above, it would have been obvious to one of ordinary skill in the art during the time of the said invention to enable a control unit operably connected to the motorized scanning mechanism, the control unit configured to automatically direct the scanning mechanism to capture images at various angles, in order to effectively capture the data necessary to effectively analyze traffic violations and the parties involved.
Claim(s) 5, 7, 8, 10, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Subramanya (US 20140210646 A1) in view of Ratti (US 20180211117 A1).
In regards to claim 5, Subramanya fails to teach the control unit is further configured to automate scanning based on detected motion of surrounding vehicles.
Ratti on the other hand teaches the control unit is further configured to automate scanning based on detected motion of surrounding vehicles (Paragraphs 16, 36)
A paper titled “Integrating motion and appearance for overtaking vehicle detection (Published in: Intelligent Vehicles Symposium Proceedings”, 2014 IEEE, DOI: 10.1109/IVS.2014.6856598,), proposes an algorithm for detecting overtaking vehicles using motion cues from the scene. Motion compensation of video data is performed using the optical flow of the scene and epipolar geometry. After post processing and outlier removal, overtaking vehicle candidates are produced.[P-16]
EP patent application EP2578464A1 titled “Video-based warning system for a vehicle” by Honda Research Institute Europe GMBH describes a warning system that can be implemented in any kind of, in order to efficiently detect moving objects. The system utilizes at least one camera for a continuous imaging of the surroundings of the vehicle. Thereby, moving objects can be monitored. A computing unit is programmed to estimate a motion of any moving object based on a pixel motion in the camera image. If a dangerously moving object is detected, a warning unit can be used for issuing a warning signal.[P-36]
Here we see Ratti teach motion cues to configure the orientation of the camera to effectively capture the object/vehicle on motion. As a result, it would have been obvious to one of ordinary skill in the art during the time of the filing date of the invention to combine Ratti’s teach with Subramanya’s teaching, in order to effectively capture an moving object in the act of a traffic violation.
In regards to claim 7, Subramanya fails to explicitly teach an edge computing unit operatively connected to the one or more cameras or said processing unit, wherein the edge computing unit employs machine learning to enhance the accuracy of traffic violation detection during local processing.
Ratti on the other hand teaches an edge computing unit operatively connected to the one or more cameras or said processing unit, wherein the edge computing unit employs machine learning to enhance the accuracy of traffic violation detection during local processing (Paragraphs 7, 25, 47)
A paper titled “License Plate Detection and Recognition Using Deeply Learned Convolutional Neural Networks” by Syed Zain Masood, et al, proposes a sighthound's fully automated license no. plate detection and recognition system. The core technology of the system is built using a sequence of deep Convolutional Neural Networks interlaced with accurate and efficient algorithms.[P-7]
US patent application US20170300763A1 titled “Road feature detection using a vehicle camera system” by GM Global Technology Operations LLC, provides a computer-implemented method for road feature detection, the method comprising receiving, by a processing device, an image from a camera system associated with a vehicle on a road; generating, by the processing device, a top view of the road based at least in part on the image; detecting, by the processing device, lane boundaries of a lane of the road based at least in part on the top view of the road; and detecting, by the processing device, a road feature within the lane boundaries of the lane of the road using machine learning.[P-25]
US patent application US20120148105A1 titled “Automated license plate recognition system and method using human-in-the-loop based adaptive learning” by Xerox Corp. describes an automated license plate recognition (ALPR) system and method using a human-in-the-loop based adaptive learning approach. One or more images with respect to an automotive vehicle can be segmented in order to determine a license plate of the automotive vehicle within a scene. An optical character recognition (OCR) engine loaded with an OCR algorithm can be further adapted to determine a character sequence of the license plate based on a training data set. A confidence level with respect to the images can be generated in order to route a low confidence image to an operator for obtaining a human interpreted image. The parameters with respect to the OCR algorithm can be adjusted based on the human interpreted image and the actual image of the license plate. A license plate design can be then incorporated into the OCR engine in order to automate the process of recognizing the license plate with respect to the automotive vehicle in a wide range of transportation related applications.[P-47]
It would have been obvious to one of ordinary skill in the art during the time of the invention to combine Ratti’s teaching with Subramanya’s teaching in order to enable the effective recognition and record thereafter the specific real time traffic scenario being observed.
In regards to claim 8, Subramanya modified via Ratti teaches the edge computing unit utilizes convolutional neural networks (CNNs) to analyze the images for detecting and predicting traffic violations (Paragraphs 75, Ratti).
The present disclosure relates to artificial intelligence based systems and method for determination of traffic violations. The present disclosure provides systems and methods that use deep convolutional neural networks and machine vision based algorithms to perform a task of detection and recognition to provide complete solution to safe, legal and comfortable parking, driving and riding for commuters on the roadways. Roadway stewardship systems, Parking management systems when made on-demand and crowdsourced, can play a very strong role in regulating driving conditions in cities and highways. By allowing the on-demand, crowdsourced, roadway stewardship system to be automated, through the use of Artificial Intelligence (AI) sub-systems, users can be trained to recognize and be educated as well in the laws & regulations around the use of roadways; can help the process through an interactive console/game-play, which can also be used for monetization for individuals to earn money for their contribution. The AI assisted with Human Intelligence (HI) together called HAI in particular, can play a valuable role in reducing traffic density, traffic movement restrictions and fuel and time waste in large cities. Also proper driving on the roads can lead to faster and safer commute. In Addition, multiple other objects of interest can also be identified and trained to be recognized using the Stewardship System disclosed herein.[Abstr]
U.S. Pat. No. 9,286,524B titled “Multi-task deep convolutional neural networks for efficient and robust traffic lane detection” assigned to University of Technology Sydney, Toyota Motor Corp provides a computing device comprising of one or more processors for controlling operations of the computing device; and a memory storing data and program instructions used by the one or more processors,[P-22]
A paper titled “Traffic Sign Detection based on Convolutional Neural Networks”, by Y. Wu et. al., provides an approach for traffic sign detection based on Convolutional Neural Networks (CNN) by first transforming the original image into the gray scale image by using support vector machines, then use convolutional neural networks with fixed and learnable layers for detection and recognition. The fixed layer can reduce the amount of interest areas to detect, and crop the boundaries very close to the borders of traffic signs. The learnable layers can increase the accuracy of detection significantly.[P-75]
In regards to claim 10, Subramanya modified via Ratti teaches monitoring real-time the behavior of the distracted driver modified teaches edge computing unit further employs object detection algorithms to identify distracted driving behaviors (Paragraph 184, Ratti)
FIG. 14: Shows how the phone can capture the sight direction of people in the car and the location of the advertising boards, traffic signs and other displays on the road. The system determines if the visual markers are indeed watched by people, can rate their quality of placement or determine if they are distracting, causing accidents/bad driving[P-184]
In regards to claim 11, Subramanya modified teaches the edge computing unit is configured to apply anomaly detection algorithms to identify unusual driver behaviors, such as usage of mobile devices while driving (Paragraph 201, Subramanya)
FIG. 3: A. without adequate lights 39, showing the license plates forces another violation trigger. B. instead of being offensive, some number plates are just generally in the wrong format, but not offensive. Wherein the size of the font of the angle of the font (italics etc.) or just the spacing between letter and number isn't standard. C. for transportation vehicles it is not authorized to carry more than 6 persons on the same vehicle. In such cases, the ANNs can count the number of people riding and the type of vehicle and flag the vehicle for violating the rule. D. driving by a type of person who is unable to properly sit on the vehicle is detected by the ANNs and flagged. E. for motorists on 2-wheel motor vehicles, wearing a helmet is mandatory; hence any deviation from the law can flag the ANN to cite the violation. F. smoke coming from the vehicle can be detected by the ANNs by creating a bounding box around the smoke and the bounding box of the vehicle ROI and an intersection of the two would confirm a violation. In case multiple vehicles are traveling together, then the detection can be in category 3, where in the multiple images would show the smoke belonging to one vehicle. G. Tires also not in good health or the wrong size, or driving on low air/gas would be detected by the ANNs when the vehicle comes to a stop. Deflated tyres can be pickup up while driving as well. H. Vehicles with only one head light would be picked up by the ANNs again by training them against vehicles with both headlights. I. Shows motorists using a mobile phone while driving which is a grave distraction and is illegal is mostly all cities. ANNs can detect conditions when a mobile phone is being operated by observing the location of the hands, the visuals of the mobile phone.[P-201]
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Subramanya (US 20140210646 A1) in view of Ratti (US 20180211117 A1) as applied to claim 5 above, and further in view of Nara et al. (CN 111801663 A).
In regards to claim 6, Subramanya modified fails to teach the processing unit is configured to, in response to a user input, override the automated scanning function for targeted image capture.
Nara on the other hand teaches the processing unit is configured to, in response to a user input, override the automated scanning function for targeted image capture (Page 12, Paragraphs 5, 6)
The on-vehicle device 1 further comprises an operation input unit 40 for accepting a cancel operation for any one of a plurality of auxiliary processes input by a user in the list image 420. The auxiliary processing section 18 accepts the auxiliary processing 421 (FIG. 8) of the cancel operation from the execution object removing operation input section 40. In this way, the user can easily remove unnecessary auxiliary processing from the execution object.[Pg 12, P-5]
(6) the operation input unit 40 can input the operation of cancelling the cancel operation from the user. When the cancel operation cancel operation is input to the operation input part 40, the auxiliary processing unit 18 makes the auxiliary processing from the execution object excluded to the execution object. In this way, the auxiliary processing that the user can temporarily remove from the execution object again becomes the execution object.[Pg 12, P-6]
Here we see the user input overriding/canceling an auxiliary processing input, which includes the operation of capturing a desired target Image related to the vehicle environment). Using this operative configuration, one of ordinary skill in the art may obviously configure the operation to yield similarly, the processing unit is configured to, in response to a user input, override the automated scanning function for targeted image capture.
It would have been obvious to one of ordinary skill in the art to combine Nara’s teaching with Subramanya’s teaching, in order to effectively customizably configure the operation of the environmental tracking of the vehicle for safety operations .
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Subramanya (US 20140210646 A1) in view of Ratti (US 20180211117 A1) as applied to claim 8 above, and further in view of Trundle et al. (US10836309 B1)
In regards to claim 9, Subramanya modified fails to teach the edge computing unit incorporates recurrent neural networks (RNNs) to analyze temporal sequences of images for detecting patterns indicative of traffic violations over time.
Trundle on the other hand teaches the edge computing unit incorporates recurrent neural networks (RNNs) to analyze temporal sequences of images for detecting patterns indicative of traffic violations over time (Column 8, line 65-Column 9, line 3)
In some implementations, a recurrent neural network (RNN) or another deep neural network (DNN) that is structured to handle sequences of data is trained to process each frame in turn, such that the machine learning includes a temporal context, e.g., to monitor real-time the behavior of the distracted driver.[Col 8, ln 65-Col 9, ln 3]
It would have been obvious to one of ordinary skill in the art during the time of the filing date of the invention to combine Trundle’s teaching with Subramanya modified’s teaching in order to enable the effective monitoring real-time the behavior of the distracted driver.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTHONY D AFRIFA-KYEI whose telephone number is (571)270-7826. The examiner can normally be reached Monday-Friday 10am-7pm.
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/ANTHONY D AFRIFA-KYEI/Examiner, Art Unit 2686
/BRIAN A ZIMMERMAN/Supervisory Patent Examiner, Art Unit 2686
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