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.
Status of Claims
In the amendment filed on February 25th, 2026, claims 1 and 18 have been amended, no claim has been cancelled and no new claim has been added. Therefore, claims 1-14, 16-20 are pending for examination.
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) 1, 3, 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 10573183 B1) in view of Stenneth et al. (US 20230140584 A1), Stempora (US 20150019266 A1) and Li et al (CN 114084170 A)
In regards to claim 1, Li teaches a method for autonomously monitoring traffic on a road comprising: a vehicle traffic monitoring device viewing scenes outside the vehicle traffic monitoring device and generating live video of the viewed scenes(Column 3, lines 23-34; Figure 1B; Column 3, line 64-Column 4, line 8; Column 13, lines 37-42)
For example, FIG. 1B illustrates an exemplary hazard view 10 which may be displayed on display 109 of electronic device or mobile device 102. Hazard view 10 may display a live video feed of driving scenes 112 collected from camera 108 along with optional augmented reality elements to warn the user of potential hazards. The hazard view 10 may help draw the attention of the user or driver to potential hazards which the user may not have noticed. The hazard view may include interface elements such as hazard indicator 11, hazard marker 12, and hazard route overlay 13. The hazard view 10 may be triggered or activated by detection of a hazard or potential hazardous event. [Col 3, ln 23-34]
In one example, the potential hazards detected by the mobile device 102, based on at least the mobile device 102's sensors 106, images from camera 108, can include proximity to a road object (e.g. tailgating above a particular speed) or potential contact with a car, bike, pedestrian, other road objects, or non-road objects or road-adjacent objects. The potential hazards can also include the detecting and warning of the vehicle swerving lanes, swerving out of lane, rolling stops when the mobile device 102 detects a stop sign in the path of the vehicle, failure to stop at a stop sign or red light, speeding, or running yellow lights that turned red before the vehicle crossed the lane or running red lights.[Col 3, line 64-Col 4, ln 8]
It is understood that some or all steps, operations, or processes may be performed automatically, without the intervention of a user. Method claims may be provided to present elements of the various steps, operations or processes in a sample order, and are not meant to be limited to the specific order or hierarchy presented.[Col 13, ln 37-42]
Li also teaches the vehicle traffic monitoring device performing object recognition on frames of the live video in real time using computer vision techniques; the vehicle traffic monitoring device making one or more determinations whether there exists a potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic based on the object recognition(Column 3, lines 23-34; Figure 1B; Column 3, lines 39-53, line 64-Column 4, line 8; Column 8, lines 29-41)
For example, FIG. 1B illustrates an exemplary hazard view 10 which may be displayed on display 109 of electronic device or mobile device 102. Hazard view 10 may display a live video feed of driving scenes 112 collected from camera 108 along with optional augmented reality elements to warn the user of potential hazards. The hazard view 10 may help draw the attention of the user or driver to potential hazards which the user may not have noticed. The hazard view may include interface elements such as hazard indicator 11, hazard marker 12, and hazard route overlay 13. The hazard view 10 may be triggered or activated by detection of a hazard or potential hazardous event. [Col 3, ln 23-34]
Hazard indicator 11 is a graphical indication designed to draw attention to the existence and location of a potential hazard. The hazard indicator may be of any shape or object. It may also be of any color or change colors. The hazard indicator may be displayed in a manner that associates the hazard indicator with the potential hazard. In some embodiments, the hazard indicator may include a short textual description such as “warning”, “pedestrian”, or “merging vehicle”, or a short textual instruction such as “slow down”, “watch left”, or “avoid right”. In some embodiments, multiple hazard indicators may be displayed when there are multiple hazards. In other embodiments a single hazard indicator may be displayed. When a single hazard indicator is displayed, it may be associated with the most urgent or dangerous hazard.[Col 3, ln 39-53]
In one example, the potential hazards detected by the mobile device 102, based on at least the mobile device 102's sensors 106, images from camera 108, can include proximity to a road object (e.g. tailgating above a particular speed) or potential contact with a car, bike, pedestrian, other road objects, or non-road objects or road-adjacent objects. The potential hazards can also include the detecting and warning of the vehicle swerving lanes, swerving out of lane, rolling stops when the mobile device 102 detects a stop sign in the path of the vehicle, failure to stop at a stop sign or red light, speeding, or running yellow lights that turned red before the vehicle crossed the lane or running red lights.[Col 3, line 64-Col 4, ln 8]
At block 204, an application such as mobile driving safety application 110 analyze the obtained images of driving scenes 112 data in conjunction with the obtained sensor data to identify, during driving (e.g., at the current time), an object in proximity of the vehicle, the vehicle's position on the road (e.g., lane centering), and/or distances to other vehicles, pedestrians, cyclists, and/or other identified objects in proximity of the vehicle. Identification of the object based on the obtained images and obtained sensor may be performed using, for example, an object detection or semantic segmentation machine learning model. The machine learning models may comprise a neural network with one or more convolutional neural network layers.[Col 8, ln 29-41]
Here we see Li disclose object recognition on frames of the live video in real time using computer vision techniques, i.e. semantic segmentation to be able to detect potential hazard such as objects, pedestrians and such
the vehicle traffic monitoring device including, in electronic reports, data indicative of the one or more determinations whether there exists a potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic based on generated live video (Column 4, lines 48-62; Column 4, ln 63- Column 5, ln 19; Column 7, line 57-Column 8, line 4]
Application 110 may generate, store, and/or send to the driver 114 or a third party a summary of driving safety behavior data after each trip and/or on-demand. The summary may include alert summaries for each alert that was detected during driving (e.g., including a type of alert, vehicle speed, acceleration, and/or other motion features at the time of the alert, and/or an image of the road and/or roadside objects associated with the alert and captured by the mobile device camera before, during, and/or after the alert). The sensor data collected from sensors 106 and image data collected from camera 108, such as in the form of video data, may be stored per driving session in addition to the list of alerts and hazards per driving session. In some examples, a driving session may comprise a trip from a start location to an end location.[Col 4, ln 48-62]
In one example, the summary can include a list of each specific alert or potential hazards detected during the driving session. For example, the summary displayed to the user can include a driving summary number indicating the total number of lanes swerved during the driving session and include a list of each of the specific instances of the driver swerving lanes with timestamps of each instance. In one example, the driving summary can also display, along with the information on swerving lanes, a list of each instance of the driver speeding past a predetermined speed depending on the predetermined speed of the location of the vehicle and display it to the driver. Potential hazards may also be tracked, including pedestrians, other vehicles, bicycles, animals, construction work, and other hazards, according to where they were encountered during the driving session. In one example, for each alert or potential hazard a total number of alerts for the specific type of alert or potential hazard and/or each specific instance of the specific type of alert may be displayed. In one example, for each alert or potential hazard additional information may be displayed such as a timestamp, location, image captured during the alert or potential hazard, or video captured during the alert or potential hazard. Other alerts and potential hazards may also be output.[Col 4, ln 63-Col 5, ln 19]
In some examples, drive summary data of one or more users may be provided to third parties, such as insurance companies, for risk assessment purposes, such as determining the premium price of a vehicle insurance policy for the user based on his or her past driving performance in the drive summary. The data provided in the drive summary may be of any of the types described herein, such as longitudinal, population data, cohort data, risk profiles, raw data, sensor data, and other data. In some examples, the drive summary data is made available to third parties through an application programming interface (API). In some examples, the price of a vehicle insurance premium is generated by a machine learning model, such as a neural network, based on accepting at least a portion of the drive summary as input.[Col 7, ln 57-Col 8, ln 4]
Here, we see Li describing an electronic report generated and transmitted to a server for retrieval by third parties such as insurance agencies, the electronic report comprises data related to traffic hazards including metadata and video data with specific timestamps.
Li also teaches the vehicle traffic monitoring device transmitting the electronic reports during an occurrence of the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic via a wireless cellular network connection module of the vehicle traffic monitoring device (Column 8, lines 54-62); Column 9, lines 62- Column 10, line 3)
At block 208, the images, sensor data, hazard information, alert information, and/or other information (e.g., GPS information) may be sent to a remote server such as server 104 (e.g., over a cellular, WiFi, or other communications network). The images, sensor data, hazard information, alert information, and/or other information may be sent in real time to the server or may be stored at the mobile phone or vehicle for bulk upload to the server upon connection to a computer or WiFi network.[Col 8, ln 54-62]
At block 211, the drive summary, along with the images, sensor data, hazard information, alert information, and/or other information (e.g., GPS information) may be sent to a remote server such as server 104 (e.g., over a cellular, WiFi, or other communications network). The images, sensor data, hazard information, alert information, and/or other information may be sent in real time to the server or may be stored at the mobile phone or vehicle for bulk upload to the server upon connection to a computer or WiFi.[Col 9, ln 62 -Col 10, ln 3]
Li fails to teach the live video captures being at least in a high-definition (HD) resolution.
Stenneth on the other hand teaches live video captures of traffic scenes during vehicular motion being at least in a high-definition (HD) resolution. (Paragraph 50, 79)
If the light-based attributes of a road segment satisfy the first set, the second set, or the third set of thresholds, the light-based mapping platform 223 may: (1) a notification indicating an adverse condition corresponding to each set of thresholds that is satisfied; (2) generate a route based on the road segment (e.g., generate a route that avoids the road segment); (3) cause vehicles that are traversing or will be traversing the road segment to switch from a first set of sensors to a second set of sensors (e.g., changing from lidar to high-definition cameras); (4) cause autonomous or semi-autonomous vehicles that are traversing or will be traversing the road segment to switch a way of which the vehicles are being maneuvered (e.g., switching from autonomous mode to manual mode); (5) update the table to include an indication of the adverse road condition (e.g., a flag); or (6) a combination thereof. In one embodiment, if the light-based mapping platform 223 determines that a vehicle will be traversing such road segment, the light-based mapping platform 223 may allow the vehicle to traverse the road segment if the vehicle is following a leading vehicle that has predetermined attributes and will also be traversing the road segment. By way of example, such predetermined attributes may indicate that the leading vehicle has physical characteristics that may assist in mitigating the adverse road condition for the following vehicle (e.g., rear dimensions of the leading vehicle large enough to mitigate a glare from sunlight). In one embodiment, if the light-based mapping platform 223 determines that a vehicle will be traversing a road segment that satisfies the first and/or the third set of thresholds, the light-based mapping platform 223 may cause one or more drones to be deployed to the road segment to mitigate the adverse road condition. By way of example, if the road segment is impacted with a glare from sunlight, the one or more drones may be instructed to block the glare for the vehicle, or if the road segment is impacted with an intense ray of light, the one or more drones may be instructed to block the ray of light for the vehicle and/or provide a heat resisting solution (e.g., water) to the vehicle.[P-50]
The action module 409 generates commands based on analysis executed by the calculation module 403. By way of example, if the calculation module 403 determines that a road segment includes an adverse road condition, the action module 409 may: (1) cause vehicles that are traversing or will be traversing the road segment to switch from a first set of sensors to a second set of sensors (e.g., changing from lidar to high-definition cameras); (2) cause autonomous or semi-autonomous vehicles that are traversing or will be traversing the road segment to switch a way of which the vehicles are being maneuvered (e.g., switch from autonomous mode to manual mode); (3) cause one or more drones to be deployed to the road segment to mitigate the adverse road condition (e.g., if the road segment is impacted with a glare from sunlight, the one or more drones may be instructed to block the glare for the vehicle, or if the road segment is impacted with an intense ray of light, the one or more drones may be instructed to block the ray of light for the vehicle and/or provide a heat resisting solution to the vehicle); or (4) a combination thereof.[P-79]
Therefore, it would have been obvious to one of ordinary skill in the art during the filing date of the invention to combine Stenneth’s teaching with Li’s teaching in order to improve and optimize to enable a more effective means to capture and detect objects and hazards on a vehicles road path
Furthermore, Li modified fails to teach the vehicle traffic monitoring device is controlled by a mobile operator network via a connected module.
Stempora on the other hand teaches a network of traffic monitoring sensors affixed to moving vehicles as well as wayside unit and infrastructure, that may be controlled by a remote device/module by a third party operator via mobile device via a network. Data from the cameras located within the vehicle and the infrastructure may be analyzed and utilized in the influence of the control of cameras by the third party (Paragraphs 34, 62, 116, 120)
On or more algorithms may be executed within the framework of a software application (such as a software application installed on a portable cellular phone device) that may provide information to an external server or communicate with an external server or processor that executes one or more algorithms or provides information for one or more algorithms to be executed by a processor on the portable device. One or more static or dynamic methods for providing or generating risk assessment, risk scoring, loss control, risk information, evaluating vehicle operation performance, monitoring vehicular operator behavior, monitoring portable device use behavior, providing insurance related information or adjusting the price of insurance, responding to increased operational risk for an operator of a vehicle, evaluating cognitive ability of a driver, evaluating level of distraction while driving, or other operations performed by other algorithms disclosed herein may be executed by one or more algorithms, software components, or software applications on one or more processors of the portable device and/or vehicle, a processor remote from the portable device and/or vehicle, or a processor in operative communication with the portable device and/or vehicle.[P-34]
In one embodiment, a method of analyzing risk comprises correlating driving performance with the operation of a portable device; correlating driving performance with operation of a specific application, software or function on the portable device; or analyzing the individual cognitive effort required to operate the portable device while operating the vehicle. The vehicle operation performance may be analyzed using a vehicle operation performance algorithm. The vehicle operation performance algorithm input can include information originating from one or more vehicle sensors, vehicle human interface components, portable device sensors, portable device human interface components, or devices external to the vehicle (such as speeding cameras, traffic violation reports, external map information, another vehicle, vehicle infrastructure network or exchange, or weather information, for example). For example, the vehicle operation performance analysis performed by the vehicle operation performance algorithm may include input such as accident information, speeding data, swerving information, safe driving, unsafe driving, location, route choice, parking violations, average cognitive load during a trip, or traffic information. In one embodiment, the vehicle operation performance algorithm correlates the temporal movement information with other vehicle operation performance algorithm input information to evaluate the vehicle operator performance.[P-62]
In one embodiment, the portable device function modification algorithm comprises input in the form of historical information, current information, or predicted future information from one or more selected from the group: the vehicle operation performance algorithm; the cognitive analysis algorithm; the movement isolation algorithm; one or more sensors on the vehicle, portable device, and/or a remote device; one or more user interface components of the vehicle and/or portable device; and/or devices or servers external to the vehicle (such as servers providing data from speeding cameras, traffic violation reports, external map information, weather information, statistical or raw vehicle operation data from the current operator (such as historical vehicle operation performance for the operator), or statistical or raw vehicle operation data from other vehicle operators). In one embodiment, the modification policy or restriction is determined by the operator or owner of the portable device or vehicle, a third party (such as a parent or guardian, a business supervisor, or insurance company) and may be configured on the portable device, controlled by a remote server (such as a third party server for an insurance company), or managed by the operator of the portable device and/or vehicle.[P-116]
In one embodiment, the portable device software restriction algorithm comprises input in the form of historical information, current information, or predicted future information from one or more selected from the group: the vehicle operation performance algorithm; the cognitive analysis algorithm; the movement isolation algorithm; one or more sensors on the vehicle, portable device, and/or a remote device; one or more user interface components of the vehicle and/or portable device; and/or devices or servers external to the vehicle (such as servers providing data from speeding cameras, traffic violation reports, external map information, weather information, statistical or raw vehicle operation data from the current operator (such as historical vehicle operation performance for the operator), or statistical or raw vehicle operation data from other vehicle operators). In one embodiment, the restriction is determined by the operator or owner of the portable device or vehicle, a third party (such as a parent or guardian, a business supervisor, or insurance company) and may be configured on the portable device, controlled by a remote server (such as a third party server for an insurance company), or managed by the operator of the portable device and/or vehicle.[P-120]
It would therefore be obvious to one of ordinary skill in the art to combine Stempora’s teaching with Li modified’s teaching in order to enable a more effective means to evaluate several vehicles driving behavior and policing thereafter
Li modified fails to teach the vehicle traffic monitoring device is integrated into a streetlamp and is controlled by a cellular network to which the wireless cellular network connection module is connected.
Li ‘4170 on the other hand teaches the vehicle traffic monitoring device is integrated into a streetlamp and is controlled by a mobile network operator of a cellular network to which the wireless cellular network connection module is connected.(page 7, Paragraph 7)
VIU and provided with, for example, a roadside laser radar, video camera radar, an edge calculating device, a road side sensing result generating device, a roadside communication device, intelligent road side device with traffic control and operation function and/or intelligent traffic signal lamp intelligent network link road side server through communication network (e.g., 4G, 5G, 6G, 7G cellular network; special short distance communication technology DSRC or C-V2X) establishing connection and logging in the cloud system. The support module of the VIU maximizes the security and reliability of the communication network and the user privacy, and ensures the reliable and stable power supply of each module of the VIU. the communication mode of the VIU uses 4G, 5G, 6G and/or a 7 G cellular network; special short range communication technology (DSRC); and/or C-V2X technology, for communication and/or interaction with other vehicles equipped with VIU sub-system and/or cloud system.[Pg 7, P-7]
Here we see street lamp infrastructure capable of determining a traffic situation and thereafter transmitting warnings/notifications to vehicles via a cellular network controlled by mobile network operator. Thereby when combined with Li modified via Stempora’s teaching, one of ordinary skill in the art may control the said cameras integrated within the streetlamps to retrieve traffic information, alongside from cameras from the vehicle by way of a mobile device configured and communicated via a cellular network.
It would have been obvious to one of ordinary skill in the art to combine Li ‘4170 teaching with Li modified’s teaching in order to enable a more effective way to alert vehicles about a respective traffic hazard.
In regards to claim 3, Li modified teaches in response to determining there exists a potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic, causing an alert to be generated for one or more drivers of one or more vehicles determined to be potentially affected by the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic(Column 3, lines 23-34; Figure 1B; Column 3, line 64-Column 4, line 8)
For example, FIG. 1B illustrates an exemplary hazard view 10 which may be displayed on display 109 of electronic device or mobile device 102. Hazard view 10 may display a live video feed of driving scenes 112 collected from camera 108 along with optional augmented reality elements to warn the user of potential hazards. The hazard view 10 may help draw the attention of the user or driver to potential hazards which the user may not have noticed. The hazard view may include interface elements such as hazard indicator 11, hazard marker 12, and hazard route overlay 13. The hazard view 10 may be triggered or activated by detection of a hazard or potential hazardous event. [Col 3, ln 23-34]
In one example, the potential hazards detected by the mobile device 102, based on at least the mobile device 102's sensors 106, images from camera 108, can include proximity to a road object (e.g. tailgating above a particular speed) or potential contact with a car, bike, pedestrian, other road objects, or non-road objects or road-adjacent objects. The potential hazards can also include the detecting and warning of the vehicle swerving lanes, swerving out of lane, rolling stops when the mobile device 102 detects a stop sign in the path of the vehicle, failure to stop at a stop sign or red light, speeding, or running yellow lights that turned red before the vehicle crossed the lane or running red lights.[Col 3, line 64-Col 4, ln 8]
In regards to claim 18, Li modified teaches the vehicle traffic monitoring device performing object recognition on frames of generated live video using computer vision techniques to make one or more determinations whether there exists a potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic based on generated live video includes(Column 3, lines 23-34; Figure 1B; Column 3, lines 39-53, line 64-Column 4, line 8; Column 8, lines 29-41)
For example, FIG. 1B illustrates an exemplary hazard view 10 which may be displayed on display 109 of electronic device or mobile device 102. Hazard view 10 may display a live video feed of driving scenes 112 collected from camera 108 along with optional augmented reality elements to warn the user of potential hazards. The hazard view 10 may help draw the attention of the user or driver to potential hazards which the user may not have noticed. The hazard view may include interface elements such as hazard indicator 11, hazard marker 12, and hazard route overlay 13. The hazard view 10 may be triggered or activated by detection of a hazard or potential hazardous event. [Col 3, ln 23-34]
Hazard indicator 11 is a graphical indication designed to draw attention to the existence and location of a potential hazard. The hazard indicator may be of any shape or object. It may also be of any color or change colors. The hazard indicator may be displayed in a manner that associates the hazard indicator with the potential hazard. In some embodiments, the hazard indicator may include a short textual description such as “warning”, “pedestrian”, or “merging vehicle”, or a short textual instruction such as “slow down”, “watch left”, or “avoid right”. In some embodiments, multiple hazard indicators may be displayed when there are multiple hazards. In other embodiments a single hazard indicator may be displayed. When a single hazard indicator is displayed, it may be associated with the most urgent or dangerous hazard.[Col 3, ln 39-53]
In one example, the potential hazards detected by the mobile device 102, based on at least the mobile device 102's sensors 106, images from camera 108, can include proximity to a road object (e.g. tailgating above a particular speed) or potential contact with a car, bike, pedestrian, other road objects, or non-road objects or road-adjacent objects. The potential hazards can also include the detecting and warning of the vehicle swerving lanes, swerving out of lane, rolling stops when the mobile device 102 detects a stop sign in the path of the vehicle, failure to stop at a stop sign or red light, speeding, or running yellow lights that turned red before the vehicle crossed the lane or running red lights.[Col 3, line 64-Col 4, ln 8]
At block 204, an application such as mobile driving safety application 110 analyze the obtained images of driving scenes 112 data in conjunction with the obtained sensor data to identify, during driving (e.g., at the current time), an object in proximity of the vehicle, the vehicle's position on the road (e.g., lane centering), and/or distances to other vehicles, pedestrians, cyclists, and/or other identified objects in proximity of the vehicle. Identification of the object based on the obtained images and obtained sensor may be performed using, for example, an object detection or semantic segmentation machine learning model. The machine learning models may comprise a neural network with one or more convolutional neural network layers.[Col 8, ln 29-41]
Li further teaches detecting a potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic based on the object detection recognizing in the frames of the generated live video one or more incidents on or near the road in real-time while the incident is occurring, the incident including one or more of: a traffic accident; a traffic collision; a road hazard; debris in the road; a hole in the road; a large crack, bump or missing pavement in the road; pooling water in the road; pieces of tire in the road; an oversized vehicle on the road; fog; rain; snow, sleet; hail; precipitation; inclement weather; a flood; high winds; a tornado; a storm; lightening; thunder; ice; reckless driving; speeding; a stalled vehicle; a roadblock; construction; one or more people approaching the road; one or more people in, on or near the road; an active shooter; one or more animals approaching the road; one or more animals in, on or near the road; a fire; one or more emergency vehicles; a vehicle pile-up; emergency vehicle lights flashing, a barricade; a traffic stop; a sign indicating a traffic issue or road condition; a speed trap; a police vehicle; and an incident on a side of the road. (Column 3, lines 23-34; Figure 1B; Column 3, lines 39-53, line 64-Column 4, line 8; Column 8, lines 29-41)
For example, FIG. 1B illustrates an exemplary hazard view 10 which may be displayed on display 109 of electronic device or mobile device 102. Hazard view 10 may display a live video feed of driving scenes 112 collected from camera 108 along with optional augmented reality elements to warn the user of potential hazards. The hazard view 10 may help draw the attention of the user or driver to potential hazards which the user may not have noticed. The hazard view may include interface elements such as hazard indicator 11, hazard marker 12, and hazard route overlay 13. The hazard view 10 may be triggered or activated by detection of a hazard or potential hazardous event. [Col 3, ln 23-34]
Hazard indicator 11 is a graphical indication designed to draw attention to the existence and location of a potential hazard. The hazard indicator may be of any shape or object. It may also be of any color or change colors. The hazard indicator may be displayed in a manner that associates the hazard indicator with the potential hazard. In some embodiments, the hazard indicator may include a short textual description such as “warning”, “pedestrian”, or “merging vehicle”, or a short textual instruction such as “slow down”, “watch left”, or “avoid right”. In some embodiments, multiple hazard indicators may be displayed when there are multiple hazards. In other embodiments a single hazard indicator may be displayed. When a single hazard indicator is displayed, it may be associated with the most urgent or dangerous hazard.[Col 3, ln 39-53]
In one example, the potential hazards detected by the mobile device 102, based on at least the mobile device 102's sensors 106, images from camera 108, can include proximity to a road object (e.g. tailgating above a particular speed) or potential contact with a car, bike, pedestrian, other road objects, or non-road objects or road-adjacent objects. The potential hazards can also include the detecting and warning of the vehicle swerving lanes, swerving out of lane, rolling stops when the mobile device 102 detects a stop sign in the path of the vehicle, failure to stop at a stop sign or red light, speeding, or running yellow lights that turned red before the vehicle crossed the lane or running red lights.[Col 3, line 64-Col 4, ln 8]
At block 204, an application such as mobile driving safety application 110 analyze the obtained images of driving scenes 112 data in conjunction with the obtained sensor data to identify, during driving (e.g., at the current time), an object in proximity of the vehicle, the vehicle's position on the road (e.g., lane centering), and/or distances to other vehicles, pedestrians, cyclists, and/or other identified objects in proximity of the vehicle. Identification of the object based on the obtained images and obtained sensor may be performed using, for example, an object detection or semantic segmentation machine learning model. The machine learning models may comprise a neural network with one or more convolutional neural network layers.[Col 8, ln 29-41]
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 10573183 B1) in view of Stenneth et al. (US 20230140584 A1), Stempora (US 20150019266 A1) and Li et al (CN 114084170 A) as applied to claim 1 above, and further in view of Cheraghi et al. (US 20220368972 A1).
In regards to claim 2, Li modified fails to teach the vehicle traffic monitoring device streaming the generated live video via the wireless cellular network connection module of the vehicle traffic monitoring device in real-time at no less than 24 frames per second, with at least about a 15 Mbps data rate and with a latency rate no greater than about than 50 milliseconds
Cheraghi on the other hand teaches a vehicle traffic monitoring device streaming the generated live video via the wireless cellular network connection module of the vehicle traffic monitoring device in real-time at no less than 24 frames per second, with at least about a 15 Mbps data rate and with a latency rate no greater than about than 50 milliseconds (Paragraph 31)
According to increased number and bandwidth of cameras/video streaming devices and the limited physical resources of the in-vehicle network 100 including the wireless in-vehicle networks for the transmission of the video streams, i.e., frequency-time resources, the video streams may be compressed before being transmitted to the in-vehicle CPU 102. That is, the video streams may be compressed to accommodate the bandwidth of the wireless in-vehicle communication. For example, an uncompressing frame of a video stream of 1280×960 pixels with 24 bits per pixel may have a bandwidth of (Frame_Height)×(Frame_Width)×(Bits_Per_Pixel)=1280×960×24=29.49 megabits (Mb), and with the video stream at 30 frames per second (fps), the wireless in-vehicle may need to have 1280×960×30×24=884.74 megabits per second (Mbps) of network data transmission speed to support that one video stream alone. In some aspects, the frames of video streams may be compressed to accommodate the network bandwidth and the network data transmission speed. In some aspects, a resource allocation scheme may be provided to be adapted for compressed video streams, which may provide a more efficient distribution of resources and reduce the latency in the wireless in-vehicle communication.[P-31]
Thereby, it would be obvious to one of ordinary skill in the art to combine Cheraghi’s teaching with Li modified’s teaching in order to enable a more seamless display of captured traffic video and resolution.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 10573183 B1) in view of Stenneth et al. (US 20230140584 A1), Stempora (US 20150019266 A1) and Li et al (CN 114084170 A) as applied to claim 3 above, and further in view of Agrawal et al. (US 20230061784 A1)
In regards to claim 4, Li modified fails to teach causing an alert regarding the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic to be transmitted via the wireless cellular network connection module of the vehicle traffic monitoring device to one or more drivers of one or more vehicles determined to be potentially affected by the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic.
Agrawal on the other hand teaches causing an alert regarding the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic to be transmitted via the wireless cellular network connection module of the vehicle traffic monitoring device to one or more drivers of one or more vehicles determined to be potentially affected by the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic. (Paragraph 31)
Aspects of the present disclosure are directed to methods of monitoring and characterizing driver behavior, which may include methods of determining and/or providing alerts to an operator of a vehicle and/or transmitting remote alerts to a remote driver monitoring system. Remote alerts may be transmitted wirelessly over a wireless network to one or more servers and/or one or more other electronic devices, such as a mobile phone, tablet, laptop, desktop, etc., such that information about a driver and objects and environments that a driver and vehicle encounters may be documented and reported to other individuals (e.g., a fleet manager, insurance company, etc.). An accurate characterization of driver behavior has multiple applications. Insurance companies may use accurately characterized driver behavior to influence premiums. Insurance companies may, for example, reward risk mitigating behavior and dis-incentivize behavior associated with increased accident risk. Fleet owners may use accurately characterized driver behavior to incentivize their drivers. Likewise, taxi aggregators may incentivize taxi driver behavior. Taxi or ride-sharing aggregator customers may also use past characterizations of driver behavior to filter and select drivers based on driver behavior criteria. For example, to ensure safety, drivers of children or other vulnerable populations may be screened based on driving behavior exhibited in the past. Parents may wish to monitor the driving patterns of their kids and may further utilize methods of monitoring and characterizing driver behavior to incentivize safe driving behavior. Package delivery providers wishing to reduce the risk of unexpected delays, may seek to incentivize delivery drivers having a record of safe driving, that exhibit behaviors that correlate with successful avoidance of accidents, and the like.[P-31]
Thereby, it would be obvious to one of ordinary skill in the art during the time of the filing date of the said invention to combine Agrawal’s teaching with Li modified’s teaching in order to enable an more effective way to alert drivers and vehicles on the road about potential traffic hazards for safety purposes
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 10573183 B1) in view of Stenneth et al. (US 20230140584 A1), Stempora (US 20150019266 A1) and Li et al (CN 114084170 A) as applied to claim 3 above, and further in view of Chen et al (CN 114023088 B)
In regards to claim 5, Li modified fails to teach causing a light, including primary light generating elements of the vehicle traffic monitoring device, message or other visual indication to be activated on a pole on which the vehicle traffic monitoring device is mounted to alert drivers on the road of the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic.
Chen on the other hand teaches causing a light, including primary light generating elements of the vehicle traffic monitoring device, message or other visual indication to be activated on a pole on which the vehicle traffic monitoring device is mounted to alert drivers on the road of the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic. (Page 4, Last Paragraph- Page 5, First Paragraph)
Compared with the prior art, the beneficial effects of the invention are as follows: An intelligent street crossing signal lamp system, (1) on LED display screen, which can publicizing traffic regulations to the public through characters and simple patterns, traffic safety warning words, or the intersection real-time safety risk prompting by the calculating unit through the video stream intelligent analysis, so as to improve the pedestrian and non-motor vehicle personnel, the motor vehicle driver complies with the vegetarian of traffic, reducing the traffic accident, (2) the intelligent detecting camera high definition the visual system of pedestrian crossing signal lamp, the fact monitoring pedestrian and non-motor vehicle illegal monitoring area, the collected video image is transmitted to the calculating unit through the form of video stream, the intelligent behaviour analysis of the pedestrian and non-motor vehicle behaviour on the pedestrian crosswalk line, monitoring the illegal monitoring area of the pedestrian and the non-motor vehicle in real time, the illegal person obtaining the corresponding punishment; An intelligent over-street signal lamp taking evidence of the illegal behaviour, in the warning method: (3) through the intelligent street signal lamp system, accurately and smartly judging the pedestrian and non-motor vehicle running red light violation behaviour and networking police system for processing, the design is simple, processing efficient, making the illegal person to obtain the corresponding punishment, effectively deterring the illegal person from the source, the illegal person will put an end to such illegal acts in the future, and further the potential safety hazard will be effectively curbed; (4) intelligent street signal lamp system, with self-learning function, through the intelligent high definition video camera pedestrian and non-motor driver long-term behaviour analysis, combined with the road traffic situation of the area of itself, and the weather information obtained by the internet, according to various kinds of pedestrian and non-motor vehicle illegal behaviour, occurrence time and other parameters, the intelligent release has pertinence safety warning, the law and regulations, so that the pedestrian and non-motor vehicle driver can abandon the lucky psychology, observe the traffic rule, ensure the self-safety, the traffic is more safe and orderly.[Pg 4, Last Prgh-Pg5, P-1]
Here, Chen shows an intelligent strette lamppost capable of warning drivers and individuals using messages on a display screen indicating potential traffic hazard, traffic issue, safety issue.
Therefore, it is obvious to one of ordinary skill in the art to combine Chen’s teaching with Li modified’s teaching in order to enable a more efficient method to warn drivers and pedestrians in traffic about a potential traffic hazard such that they avoid further facing the said hazard.
Claim(s) 6 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 10573183 B1) in view of Stenneth et al. (US 20230140584 A1), Stempora (US 20150019266 A1) and Li et al (CN 114084170 A) as applied to claim 3 above, and further in view of Li et al. (CN 109237376 A)
In regards to claim 6, Li modified fails to teach causing a light, message or other visual indication to be activated on a pole or sign alongside the road before a potential traffic hazard, traffic issue or safety issue determined by the vehicle traffic monitoring device to be a root cause of traffic congestion on the road to alert drivers on the road approaching the determined root cause of traffic congestion.
Chen on the other hand teaches causing a light, message or other visual indication to be activated on a pole or sign alongside the road before a potential traffic hazard, traffic issue or safety issue determined by the vehicle traffic monitoring device(Page 4, Last Paragraph- Page 5, First Paragraph)
Compared with the prior art, the beneficial effects of the invention are as follows: An intelligent street crossing signal lamp system, (1) on LED display screen, which can publicizing traffic regulations to the public through characters and simple patterns, traffic safety warning words, or the intersection real-time safety risk prompting by the calculating unit through the video stream intelligent analysis, so as to improve the pedestrian and non-motor vehicle personnel, the motor vehicle driver complies with the vegetarian of traffic, reducing the traffic accident, (2) the intelligent detecting camera high definition the visual system of pedestrian crossing signal lamp, the fact monitoring pedestrian and non-motor vehicle illegal monitoring area, the collected video image is transmitted to the calculating unit through the form of video stream, the intelligent behaviour analysis of the pedestrian and non-motor vehicle behaviour on the pedestrian crosswalk line, monitoring the illegal monitoring area of the pedestrian and the non-motor vehicle in real time, the illegal person obtaining the corresponding punishment; An intelligent over-street signal lamp taking evidence of the illegal behaviour, in the warning method: (3) through the intelligent street signal lamp system, accurately and smartly judging the pedestrian and non-motor vehicle running red light violation behaviour and networking police system for processing, the design is simple, processing efficient, making the illegal person to obtain the corresponding punishment, effectively deterring the illegal person from the source, the illegal person will put an end to such illegal acts in the future, and further the potential safety hazard will be effectively curbed; (4) intelligent street signal lamp system, with self-learning function, through the intelligent high definition video camera pedestrian and non-motor driver long-term behaviour analysis, combined with the road traffic situation of the area of itself, and the weather information obtained by the internet, according to various kinds of pedestrian and non-motor vehicle illegal behaviour, occurrence time and other parameters, the intelligent release has pertinence safety warning, the law and regulations, so that the pedestrian and non-motor vehicle driver can abandon the lucky psychology, observe the traffic rule, ensure the self-safety, the traffic is more safe and orderly.[Pg 4, Last Prgh-Pg5, P-1]
Here, Chen shows an intelligent strette lamppost capable of warning drivers and individuals using messages on a display screen indicating potential traffic hazard, traffic issue, safety issue.
Therefore, it is obvious to one of ordinary skill in the art to combine Chen’s teaching with Li modified’s teaching in order to enable a more efficient method to warn drivers and pedestrians in traffic about a potential traffic hazard such that they avoid further facing the said hazard.
Furthermore, Li modified fails to teach the road before a potential traffic hazard, traffic issue or safety issue determined by the vehicle traffic monitoring device to be a root cause of traffic congestion on the road to alert drivers on the road approaching the determined root cause of traffic congestion.
Li et al. (CN 109237376 A) on the other hand teaches an intelligent street lamp capable of detecting traffic congestion and using data analysis of the conditions of the road of the congestion including the cause of the congestion such as inclement weather and such (Page 3, Paragraph 6)
The multifunctional intelligent street lamp is provided with a traffic flow statistics module, it can detect the congestion condition of the road a certain time so as to timely inform the other vehicle, by setting an environment monitoring module; can effectively detect the lamp body near the air quality, can effectively detect the lamp main body is through provided with a weather monitoring module, weather conditions, such as in rainy days, reminding the driver decelerating slowly, is set with the orientation module, it can effectively locate the driver. convenient driver correct road, by setting a data analysis module, it is convenient to accurately analyze the collected data, and timely feedback, is provided with intelligent control street lamp module, when the weather is brightness is dark. a brightness sensor to the controller a signal, the controller controlling the street lamp main body, is provided with charging device, it is convenient when the new energy automobile power, charging in time through the set advertisement board, it is convenient to remind the driver running the correct route, otherwise control street lamp main body is closed, by setting a power supply module capable of ensuring that each module when working, is supported by the power supply, it is convenient to make the road lamp body collecting intelligent control street lamp, environment monitoring, locating, charging, weather monitoring, traffic flow statistics, advertising and data collecting analysis as a whole, the street lamp to realize multiple functions, intelligentization.[Pg 3, P-6]
it is obvious to one of ordinary skill in the art to combine Li’s’7376 teaching with Li modified’s teaching in order to enable a more efficient method to warn drivers and pedestrians in traffic about a potential traffic hazard causing traffic congestion such that they avoid further facing the said hazard.
In regards to claim 19, Li modified teaches the vehicle traffic monitoring device performing object recognition on frames of generated live video using computer vision techniques to make one or more determinations whether there exists a potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic based on generated live video (Column 3, lines 23-34; Figure 1B; Column 3, lines 39-53, line 64-Column 4, line 8; Column 8, lines 29-41)
For example, FIG. 1B illustrates an exemplary hazard view 10 which may be displayed on display 109 of electronic device or mobile device 102. Hazard view 10 may display a live video feed of driving scenes 112 collected from camera 108 along with optional augmented reality elements to warn the user of potential hazards. The hazard view 10 may help draw the attention of the user or driver to potential hazards which the user may not have noticed. The hazard view may include interface elements such as hazard indicator 11, hazard marker 12, and hazard route overlay 13. The hazard view 10 may be triggered or activated by detection of a hazard or potential hazardous event. [Col 3, ln 23-34]
Hazard indicator 11 is a graphical indication designed to draw attention to the existence and location of a potential hazard. The hazard indicator may be of any shape or object. It may also be of any color or change colors. The hazard indicator may be displayed in a manner that associates the hazard indicator with the potential hazard. In some embodiments, the hazard indicator may include a short textual description such as “warning”, “pedestrian”, or “merging vehicle”, or a short textual instruction such as “slow down”, “watch left”, or “avoid right”. In some embodiments, multiple hazard indicators may be displayed when there are multiple hazards. In other embodiments a single hazard indicator may be displayed. When a single hazard indicator is displayed, it may be associated with the most urgent or dangerous hazard.[Col 3, ln 39-53]
In one example, the potential hazards detected by the mobile device 102, based on at least the mobile device 102's sensors 106, images from camera 108, can include proximity to a road object (e.g. tailgating above a particular speed) or potential contact with a car, bike, pedestrian, other road objects, or non-road objects or road-adjacent objects. The potential hazards can also include the detecting and warning of the vehicle swerving lanes, swerving out of lane, rolling stops when the mobile device 102 detects a stop sign in the path of the vehicle, failure to stop at a stop sign or red light, speeding, or running yellow lights that turned red before the vehicle crossed the lane or running red lights.[Col 3, line 64-Col 4, ln 8]
At block 204, an application such as mobile driving safety application 110 analyze the obtained images of driving scenes 112 data in conjunction with the obtained sensor data to identify, during driving (e.g., at the current time), an object in proximity of the vehicle, the vehicle's position on the road (e.g., lane centering), and/or distances to other vehicles, pedestrians, cyclists, and/or other identified objects in proximity of the vehicle. Identification of the object based on the obtained images and obtained sensor may be performed using, for example, an object detection or semantic segmentation machine learning model. The machine learning models may comprise a neural network with one or more convolutional neural network layers.[Col 8, ln 29-41]
Li modified fails to teach determining that a detected potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic is a root cause of traffic congestion on a road depicted in the generated live video captured by a camera of the vehicle traffic monitoring device based on the object detection, and wherein the alert indicates the determined root cause of traffic congestion.
Li et al. (CN 109237376 A) on the other hand teaches determining that a detected potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic is a root cause of traffic congestion on a road depicted in the generated live video captured by a camera of the vehicle traffic monitoring device based on the object detection, and wherein the alert indicates the determined root cause of traffic congestion. (Page 3, Paragraph 6)
The multifunctional intelligent street lamp is provided with a traffic flow statistics module, it can detect the congestion condition of the road a certain time so as to timely inform the other vehicle, by setting an environment monitoring module; can effectively detect the lamp body near the air quality, can effectively detect the lamp main body is through provided with a weather monitoring module, weather conditions, such as in rainy days, reminding the driver decelerating slowly, is set with the orientation module, it can effectively locate the driver. convenient driver correct road, by setting a data analysis module, it is convenient to accurately analyze the collected data, and timely feedback, is provided with intelligent control street lamp module, when the weather is brightness is dark. a brightness sensor to the controller a signal, the controller controlling the street lamp main body, is provided with charging device, it is convenient when the new energy automobile power, charging in time through the set advertisement board, it is convenient to remind the driver running the correct route, otherwise control street lamp main body is closed, by setting a power supply module capable of ensuring that each module when working, is supported by the power supply, it is convenient to make the road lamp body collecting intelligent control street lamp, environment monitoring, locating, charging, weather monitoring, traffic flow statistics, advertising and data collecting analysis as a whole, the street lamp to realize multiple functions, intelligentization.[Pg 3, P-6]
it is obvious to one of ordinary skill in the art to combine Li’s’7376 teaching with Li modified’s teaching in order to enable a more efficient method to capture traffic scenarios via camera and warn drivers and pedestrians in traffic about a potential traffic hazard causing traffic congestion (such as specific weather) such that they avoid further facing the said hazard via video.
Claim(s) 7-9, 12, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 10573183 B1) in view of Stenneth et al. (US 20230140584 A1), Stempora (US 20150019266 A1) and Li et al (CN 114084170 A) as applied to claim 3 above, and further in view of Slavin (US 10997430 B1)
In regards to claim 7, Li modified teaches the causing an alert to be generated: determining a location of the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic based on a location of the vehicle traffic monitoring device that made the determination whether there exists the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic based on the generated live video (Column 3, lines 9-22; Column 3, line 39-53)
When applicable, application 110 alerts the driver of a potential hazard, or of potentially risky driving behavior. For example, display 109 of the mobile phone may be used to display a map (e.g., a real-time global-positioning-system map) or a live-view that adapts to the changing location of the mobile phone and vehicle, and to display icons or other indicators of roadway and/or roadside objects (e.g., pedestrians, other vehicles, sidewalks, lane delimiters, speed limits, etc.) in the vicinity of the user (e.g., as identified in the images). Application 110 may generate a visible and/or audible alert when the vehicle is approaching, or too closely passing (e.g., within a distance threshold based on the type of object and/or the vehicle speed) one of the roadside objects or exceeding the speed limit.[Col 3, ln 9-22]
Hazard indicator 11 is a graphical indication designed to draw attention to the existence and location of a potential hazard. The hazard indicator may be of any shape or object. It may also be of any color or change colors. The hazard indicator may be displayed in a manner that associates the hazard indicator with the potential hazard. In some embodiments, the hazard indicator may include a short textual description such as “warning”, “pedestrian”, or “merging vehicle”, or a short textual instruction such as “slow down”, “watch left”, or “avoid right”. In some embodiments, multiple hazard indicators may be displayed when there are multiple hazards. In other embodiments a single hazard indicator may be displayed. When a single hazard indicator is displayed, it may be associated with the most urgent or dangerous hazard. [Col 3, ln 39-53]
Li modified fails to teach receiving location information for a plurality of drivers based on or including one or more of: GPS data associated with respective vehicles or mobile devices of the plurality of drivers;
Slavin on the other hand teaches receiving location information for a plurality of drivers based on or including one or more of: GPS data associated with respective vehicles or mobile devices of the plurality of drivers (Column 16, line 35- column 17, line 15)
In some implementations, the DDDR system 102 can determine to collect additional event data from other local (e.g., geographically relevant) sources. For example, stationary monitoring devices 514b nearby the dangerous driver 104 (e.g., traffic cameras, security cameras of nearby businesses, etc.), mobile devices 514c belonging to nearby pedestrians (e.g., mobile phones), or monitoring devices 514d for other vehicles 515, e.g., other cars, traffic helicopters, drones, autonomous vehicles, etc., nearby the dangerous driver 504. One or more of the other monitoring devices 514b, 514c, and 514d can be determined to be geographically proximate e.g., within a threshold distance of, the dangerous driver 504 using GPS location information for the one or more other monitoring devices. A threshold distance can distance between the dangerous driver event and the other local sources of event data or recipients where the additional event data collected from the other local sources could be relevant to tracking the dangerous driver event. For example, the DDDR system 102 can use GPS location data for a vehicle 515 and GPS location data for the dangerous driver vehicle 510 and/or GPS location data for vehicle 510 to determine that the vehicle 515 is within a threshold distance the dangerous driver 504, e.g., within 50 feet, 100 feet, 0.5 miles. In another example, a traffic camera 514b can be determined to be within a path that the dangerous driver 504 is taking, e.g., using the GPS location information for the dangerous driver 504 that is recorded by the onboard event recorder 512 on vehicle 510.[Col 16, ln 35- Col 17, ln 15]
Slavin further teaches determining, based on the received location information, the one or more drivers of one or more vehicles are potentially affected by the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic based on a proximity of each of the one or more drivers to a location of the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic; and causing an alert regarding the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic to be transmitted, via wireless cellular network connection module of the vehicle traffic monitoring device or one or more other vehicle traffic monitoring devices within a selectable distance from the determined location of the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic, to the one or more drivers of one or more vehicles determined to be potentially affected by the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic. (Column 16, line 35- column 17, line 15; Column 17, lines 16-29; Column 18, line 45-Column 19, line 5)
In some implementations, the DDDR system 102 can determine to collect additional event data from other local (e.g., geographically relevant) sources. For example, stationary monitoring devices 514b nearby the dangerous driver 104 (e.g., traffic cameras, security cameras of nearby businesses, etc.), mobile devices 514c belonging to nearby pedestrians (e.g., mobile phones), or monitoring devices 514d for other vehicles 515, e.g., other cars, traffic helicopters, drones, autonomous vehicles, etc., nearby the dangerous driver 504. One or more of the other monitoring devices 514b, 514c, and 514d can be determined to be geographically proximate e.g., within a threshold distance of, the dangerous driver 504 using GPS location information for the one or more other monitoring devices. A threshold distance can distance between the dangerous driver event and the other local sources of event data or recipients where the additional event data collected from the other local sources could be relevant to tracking the dangerous driver event. For example, the DDDR system 102 can use GPS location data for a vehicle 515 and GPS location data for the dangerous driver vehicle 510 and/or GPS location data for vehicle 510 to determine that the vehicle 515 is within a threshold distance the dangerous driver 504, e.g., within 50 feet, 100 feet, 0.5 miles. In another example, a traffic camera 514b can be determined to be within a path that the dangerous driver 504 is taking, e.g., using the GPS location information for the dangerous driver 504 that is recorded by the onboard event recorder 512 on vehicle 510.[Col 16, ln 35- Col 17, ln 15]
In some implementations, the DDDR system 102 can determine which monitoring devices 514b, 514c, and 514d to engage through alert 538 using, for example, map data, local traffic laws, or the like, for an area surrounding the dangerous driver 504. For example, an alert 538 may be provided to drivers of vehicles including monitoring devices 514d that are on a same side of a divided highway, but not to drivers of vehicles including monitoring devices 514d that are on an opposite side of the divided highway. In another example, the alert 538 may be provided only to drivers of vehicles including monitoring devices 514d that are moving in a same direction as the dangerous driver 504, and not to drivers of vehicles including monitoring devise 514d that are moving in cross-traffic.[Col 17, ln 16-29]
In some implementations, the DDDR system 102 can provide an alert 138 to a user device 514c, for example, through a SMS/text message, through an application running on the user device 514c, through a robocall, or the like. The one or more user devices 514b may include devices that host and display an application including an application environment. For example, a user device 514b is a mobile device that hosts one or more native applications that includes an application interface (e.g., a graphical-user interface (GUI)) through which a user of the user device 514b may interact with the DDDR system 102 and/or the home monitoring system 126. The user device 514b may be a cellular phone or a non-cellular locally networked device with a display. The user device 514b may include a cell phone, a smart phone, a tablet PC, a personal digital assistant (“PDA”), or any other portable device configured to communicate over a network and display information. For example, implementations may also include Blackberry-type devices (e.g., as provided by Research in Motion), electronic organizers, iPhone-type devices (e.g., as provided by Apple), iPod devices (e.g., as provided by Apple) or other portable music players, other communication devices, and handheld or portable electronic devices for gaming, communications, and/or data organization. The user device 514b may perform functions unrelated to the DDDR system 102, such as placing personal telephone calls, playing music, playing video, displaying pictures, browsing the Internet, maintaining an electronic calendar, etc.[Col 18, ln 45-Col 19, ln 5]
In regards to claim 8, Li modified teaches the alert includes information indicating an approximate distance from the driver to the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic based on the received receiving location information.(Column 6, line 1-14; Column 9, lines 29-36)
In one example, a ranking or score of the drive summary can be generated based on the contents of the summary such as the frequency of potential hazards, near misses, speeding, contact or potential contact with nearby objects, tailgating, or any other alerts that can be subject to the driver summary discussed above. The score can also be based on the magnitude of each type of alert. For example, an alert for a potential contact between the vehicle and another vehicle can have a different score depending on how close, in distance the potential contact was detected. A closer distanced near miss can have a different score than a further distanced near miss. In one example, both the frequency of alerts and the magnitude of the alerts can be calculated to generate the score of the drive summary[Col 6, ln 1-14]
.At block 205, an application such as mobile driving safety application 110 analyze the obtained images of driving scenes 112 data in conjunction with the obtained sensor data to identify, during driving (e.g., at the current time), an object in proximity of the vehicle, the vehicle's position on the road (e.g., lane centering), and/or distances to other vehicles, pedestrians, cyclists, and/or other identified objects in proximity of the vehicle.[Col 9, ln 29-36]
Furthermore, Slavin also teaches the alert includes information indicating an approximate distance from the driver to the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic based on the received receiving location information(Column 16, line 35- column 17, line 15; Column 17, lines 16-29)
In some implementations, the DDDR system 102 can determine to collect additional event data from other local (e.g., geographically relevant) sources. For example, stationary monitoring devices 514b nearby the dangerous driver 104 (e.g., traffic cameras, security cameras of nearby businesses, etc.), mobile devices 514c belonging to nearby pedestrians (e.g., mobile phones), or monitoring devices 514d for other vehicles 515, e.g., other cars, traffic helicopters, drones, autonomous vehicles, etc., nearby the dangerous driver 504. One or more of the other monitoring devices 514b, 514c, and 514d can be determined to be geographically proximate e.g., within a threshold distance of, the dangerous driver 504 using GPS location information for the one or more other monitoring devices. A threshold distance can distance between the dangerous driver event and the other local sources of event data or recipients where the additional event data collected from the other local sources could be relevant to tracking the dangerous driver event. For example, the DDDR system 102 can use GPS location data for a vehicle 515 and GPS location data for the dangerous driver vehicle 510 and/or GPS location data for vehicle 510 to determine that the vehicle 515 is within a threshold distance the dangerous driver 504, e.g., within 50 feet, 100 feet, 0.5 miles. In another example, a traffic camera 514b can be determined to be within a path that the dangerous driver 504 is taking, e.g., using the GPS location information for the dangerous driver 504 that is recorded by the onboard event recorder 512 on vehicle 510.[Col 16, ln 35- Col 17, ln 15]
In some implementations, the DDDR system 102 can determine which monitoring devices 514b, 514c, and 514d to engage through alert 538 using, for example, map data, local traffic laws, or the like, for an area surrounding the dangerous driver 504. For example, an alert 538 may be provided to drivers of vehicles including monitoring devices 514d that are on a same side of a divided highway, but not to drivers of vehicles including monitoring devices 514d that are on an opposite side of the divided highway. In another example, the alert 538 may be provided only to drivers of vehicles including monitoring devices 514d that are moving in a same direction as the dangerous driver 504, and not to drivers of vehicles including monitoring devise 514d that are moving in cross-traffic.[Col 17, ln 16-29]
In regards to claim 9, Li modified teaches the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic includes one or more animals approaching or on the road and the alert includes information indicating an approximate distance from the driver to the one or more animals approaching or on the road(Column 4, line 63-Col 5, ln 19; Column 8, lines 29-41)
In one example, the summary can include a list of each specific alert or potential hazards detected during the driving session. For example, the summary displayed to the user can include a driving summary number indicating the total number of lanes swerved during the driving session and include a list of each of the specific instances of the driver swerving lanes with timestamps of each instance. In one example, the driving summary can also display, along with the information on swerving lanes, a list of each instance of the driver speeding past a predetermined speed depending on the predetermined speed of the location of the vehicle and display it to the driver. Potential hazards may also be tracked, including pedestrians, other vehicles, bicycles, animals, construction work, and other hazards, according to where they were encountered during the driving session. In one example, for each alert or potential hazard a total number of alerts for the specific type of alert or potential hazard and/or each specific instance of the specific type of alert may be displayed. In one example, for each alert or potential hazard additional information may be displayed such as a timestamp, location, image captured during the alert or potential hazard, or video captured during the alert or potential hazard. Other alerts and potential hazards may also be output..[Col 4, ln 63- Col 5, ln 19]
At block 204, an application such as mobile driving safety application 110 analyze the obtained images of driving scenes 112 data in conjunction with the obtained sensor data to identify, during driving (e.g., at the current time), an object in proximity of the vehicle, the vehicle's position on the road (e.g., lane centering), and/or distances to other vehicles, pedestrians, cyclists, and/or other identified objects in proximity of the vehicle. Identification of the object based on the obtained images and obtained sensor may be performed using, for example, an object detection or semantic segmentation machine learning model. The machine learning models may comprise a neural network with one or more convolutional neural network layers.[Col 8, ln 29-41]
In regards to claim 12, Li modified teaches the alert is a notification or information appearing in within an interface or map of a navigation application running on respective mobile devices or respective vehicle infotainment systems of respective vehicles of the one or more drivers (Column 3, lines 9-21)
When applicable, application 110 alerts the driver of a potential hazard, or of potentially risky driving behavior. For example, display 109 of the mobile phone may be used to display a map (e.g., a real-time global-positioning-system map) or a live-view that adapts to the changing location of the mobile phone and vehicle, and to display icons or other indicators of roadway and/or roadside objects (e.g., pedestrians, other vehicles, sidewalks, lane delimiters, speed limits, etc.) in the vicinity of the user (e.g., as identified in the images). Application 110 may generate a visible and/or audible alert when the vehicle is approaching, or too closely passing (e.g., within a distance threshold based on the type of object and/or the vehicle speed) one of the roadside objects or exceeding the speed limit.[Col 3.ln 9-21]
In regards to claim 13, Li modified teaches the alert includes a live video stream of the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic, the live video stream originating from the vehicle traffic monitoring device (Column 3, lines 23-34)
For example, FIG. 1B illustrates an exemplary hazard view 10 which may be displayed on display 109 of electronic device or mobile device 102. Hazard view 10 may display a live video feed of driving scenes 112 collected from camera 108 along with optional augmented reality elements to warn the user of potential hazards. The hazard view 10 may help draw the attention of the user or driver to potential hazards which the user may not have noticed. The hazard view may include interface elements such as hazard indicator 11, hazard marker 12, and hazard route overlay 13. The hazard view 10 may be triggered or activated by detection of a hazard or potential hazardous event.[Col 3, ln 23-34]
Li also teaches transmitting the video alert via the wireless cellular network connection module of the vehicle traffic monitoring device(Column 8, lines 54-62); Column 9, lines 62- Column 10, line 3)
At block 208, the images, sensor data, hazard information, alert information, and/or other information (e.g., GPS information) may be sent to a remote server such as server 104 (e.g., over a cellular, WiFi, or other communications network). The images, sensor data, hazard information, alert information, and/or other information may be sent in real time to the server or may be stored at the mobile phone or vehicle for bulk upload to the server upon connection to a computer or WiFi network.[Col 8, ln 54-62]
At block 211, the drive summary, along with the images, sensor data, hazard information, alert information, and/or other information (e.g., GPS information) may be sent to a remote server such as server 104 (e.g., over a cellular, WiFi, or other communications network). The images, sensor data, hazard information, alert information, and/or other information may be sent in real time to the server or may be stored at the mobile phone or vehicle for bulk upload to the server upon connection to a computer or WiFi.[Col 9, ln 62 -Col 10, ln 3]
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 10573183 B1) in view of Stenneth et al. (US 20230140584 A1), Stempora (US 20150019266 A1) and Li et al (CN 114084170 A) as applied to claim 3 above, and further in view of Masuda et al. (CN 102859567 A)
In regards to claim 16, Li modified teaches the vehicle traffic monitoring device performing object recognition on frames of generated live video using computer vision techniques to make one or more determinations whether there exists a potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic based on generated live video (Column 3, lines 23-34; Figure 1B; Column 3, lines 39-53, line 64-Column 4, line 8; Column 8, lines 29-41)
For example, FIG. 1B illustrates an exemplary hazard view 10 which may be displayed on display 109 of electronic device or mobile device 102. Hazard view 10 may display a live video feed of driving scenes 112 collected from camera 108 along with optional augmented reality elements to warn the user of potential hazards. The hazard view 10 may help draw the attention of the user or driver to potential hazards which the user may not have noticed. The hazard view may include interface elements such as hazard indicator 11, hazard marker 12, and hazard route overlay 13. The hazard view 10 may be triggered or activated by detection of a hazard or potential hazardous event. [Col 3, ln 23-34]
Hazard indicator 11 is a graphical indication designed to draw attention to the existence and location of a potential hazard. The hazard indicator may be of any shape or object. It may also be of any color or change colors. The hazard indicator may be displayed in a manner that associates the hazard indicator with the potential hazard. In some embodiments, the hazard indicator may include a short textual description such as “warning”, “pedestrian”, or “merging vehicle”, or a short textual instruction such as “slow down”, “watch left”, or “avoid right”. In some embodiments, multiple hazard indicators may be displayed when there are multiple hazards. In other embodiments a single hazard indicator may be displayed. When a single hazard indicator is displayed, it may be associated with the most urgent or dangerous hazard.[Col 3, ln 39-53]
In one example, the potential hazards detected by the mobile device 102, based on at least the mobile device 102's sensors 106, images from camera 108, can include proximity to a road object (e.g. tailgating above a particular speed) or potential contact with a car, bike, pedestrian, other road objects, or non-road objects or road-adjacent objects. The potential hazards can also include the detecting and warning of the vehicle swerving lanes, swerving out of lane, rolling stops when the mobile device 102 detects a stop sign in the path of the vehicle, failure to stop at a stop sign or red light, speeding, or running yellow lights that turned red before the vehicle crossed the lane or running red lights.[Col 3, line 64-Col 4, ln 8]
At block 204, an application such as mobile driving safety application 110 analyze the obtained images of driving scenes 112 data in conjunction with the obtained sensor data to identify, during driving (e.g., at the current time), an object in proximity of the vehicle, the vehicle's position on the road (e.g., lane centering), and/or distances to other vehicles, pedestrians, cyclists, and/or other identified objects in proximity of the vehicle. Identification of the object based on the obtained images and obtained sensor may be performed using, for example, an object detection or semantic segmentation machine learning model. The machine learning models may comprise a neural network with one or more convolutional neural network layers.[Col 8, ln 29-41]
Li modified fails to teach detecting high temperatures on one or more regions on a vehicle in traffic or on a road surface based on analyzing infrared (IR) images from one or more IR cameras along with corresponding non-IR images comprising the generated live video; and determining whether there exists a potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic based on the detected high temperatures on one or more regions on the vehicle in traffic.
Masuda on the other hand teaches detecting high temperatures on one or more regions on a vehicle in traffic or on a road surface based on analyzing infrared (IR) images from one or more IR cameras along with corresponding non-IR images comprising the generated live video; and determining whether there exists a potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic based on the detected high temperatures on one or more regions on the vehicle in traffic.(Paragraph 31)
In this embodiment, as shown in FIG. 2 (b), camera IR and IL is set at the front of the vehicle 10, and is symmetrical about the central axis in a vehicle width center, used for imaging the front of vehicle 10. two camera IR and IL from the road are the same height are fixed on the vehicle, and light shaft of the two are parallel to each other. the infrared camera IR and IL with object temperature is higher, the intensity of the output signal is larger (i.e., gray scale of the camera image is higher).[P-31]
Here, we see Matsuda teaches vehicular camera capturing the traffic scenes outside a vehicle to which the camera has an infrared feature such that object temperatures are detected and represented on the display with larger intensity on the image, to further distinguish the potential hazardous presence. Hence when combined with Li’s teach of warning and alerting the hazardous object on the display, it provides a more reliable and effective way to detect the object on the road pathway.
Therefore, it would be obvious to one of ordinary skill in the art to combine Matsuda’s teaching with Li’ modified’s teaching in order to substitute Masuda’s camera with Li’s camera in order to more effectively yield similar results, in object detection and roads hazards.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 10573183 B1) in view of Stenneth et al. (US 20230140584 A1), Stempora (US 20150019266 A1) and Li et al (CN 114084170 A) as applied to claim 3 above, and further in view of Petrey Jr. (US 20210019645 A1)
In regards to claim 20, Li modified teaches the vehicle traffic monitoring device performing object recognition on frames of generated live video using computer vision techniques to make one or more determinations whether there exists a potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic based on generated live video includes(Column 3, lines 23-34; Figure 1B; Column 3, lines 39-53, line 64-Column 4, line 8; Column 8, lines 29-41)
For example, FIG. 1B illustrates an exemplary hazard view 10 which may be displayed on display 109 of electronic device or mobile device 102. Hazard view 10 may display a live video feed of driving scenes 112 collected from camera 108 along with optional augmented reality elements to warn the user of potential hazards. The hazard view 10 may help draw the attention of the user or driver to potential hazards which the user may not have noticed. The hazard view may include interface elements such as hazard indicator 11, hazard marker 12, and hazard route overlay 13. The hazard view 10 may be triggered or activated by detection of a hazard or potential hazardous event. [Col 3, ln 23-34]
Hazard indicator 11 is a graphical indication designed to draw attention to the existence and location of a potential hazard. The hazard indicator may be of any shape or object. It may also be of any color or change colors. The hazard indicator may be displayed in a manner that associates the hazard indicator with the potential hazard. In some embodiments, the hazard indicator may include a short textual description such as “warning”, “pedestrian”, or “merging vehicle”, or a short textual instruction such as “slow down”, “watch left”, or “avoid right”. In some embodiments, multiple hazard indicators may be displayed when there are multiple hazards. In other embodiments a single hazard indicator may be displayed. When a single hazard indicator is displayed, it may be associated with the most urgent or dangerous hazard.[Col 3, ln 39-53]
In one example, the potential hazards detected by the mobile device 102, based on at least the mobile device 102's sensors 106, images from camera 108, can include proximity to a road object (e.g. tailgating above a particular speed) or potential contact with a car, bike, pedestrian, other road objects, or non-road objects or road-adjacent objects. The potential hazards can also include the detecting and warning of the vehicle swerving lanes, swerving out of lane, rolling stops when the mobile device 102 detects a stop sign in the path of the vehicle, failure to stop at a stop sign or red light, speeding, or running yellow lights that turned red before the vehicle crossed the lane or running red lights.[Col 3, line 64-Col 4, ln 8]
At block 204, an application such as mobile driving safety application 110 analyze the obtained images of driving scenes 112 data in conjunction with the obtained sensor data to identify, during driving (e.g., at the current time), an object in proximity of the vehicle, the vehicle's position on the road (e.g., lane centering), and/or distances to other vehicles, pedestrians, cyclists, and/or other identified objects in proximity of the vehicle. Identification of the object based on the obtained images and obtained sensor may be performed using, for example, an object detection or semantic segmentation machine learning model. The machine learning models may comprise a neural network with one or more convolutional neural network layers.[Col 8, ln 29-41]
Li fails to teach the performing traffic analysis on a road depicted in the generated live video captured by a camera of the vehicle traffic monitoring device based on the object detection recognizing in the frames of the generated live video one or more characteristics of vehicles in traffic on the road, wherein the one or more characteristics include: one or more license plate characteristics of each vehicle; which states issued a license plate for each vehicle; whether a license plate is expired; type of each vehicle; make, model and year of each vehicle; how many passengers each vehicle is able to carry; how many passengers each vehicle is carrying; whether each vehicle is a taxi; whether each vehicle is a rideshare vehicle; estimated weight of each vehicle; number of times each vehicle has been detected on a particular stretch of road over a particular time period; a time of day each vehicle has been detected on a particular stretch of road; speed of traffic on particular stretch of road captured by the vehicle traffic monitoring device; and statistics regarding speed of traffic on particular stretch of road captured by the vehicle traffic monitoring device over one or more particular selectable time periods.
Petrey Jr. on the other hand teaches the performing traffic analysis on a road depicted in the generated live video captured by a camera of the vehicle traffic monitoring device based on the object detection recognizing in the frames of the generated live video one or more characteristics of vehicles in traffic on the road, wherein the one or more characteristics include: one or more license plate characteristics of each vehicle (Paragraphs 29,38, 48)
The computing system may analyze the images captured by the cameras and detect a license plate identifier (ID) of a vehicle. The license plate ID may be compared with trusted license plate IDs that are stored in a database. When there is not a trusted license plate ID that matches the license plate ID, the computing system may identify the vehicle as a suspicious vehicle. Then, the computing system may correlate the license plate ID of the vehicle with at least one of the stored electronic device identifiers. In some embodiments, the license plate ID and the at least one of the stored electronic device identifiers may be correlated with a face of the individual. In some embodiments, personal information, such as name, address, Bluetooth MAC address, WiFi MAC address, criminal record, whether the suspicious individual is on a crime watch list, etc. may be retrieved using the license plate ID or the at least one of the stored electronic device identifiers that is correlated with the license plate ID of the suspicious vehicle.[P-29]
In some embodiments, the cameras 120 may be located in the license plate detection zones 122. Although just one camera 120 and one license plate detection zone 122 are depicted, it should be noted that any suitable number of cameras 120 may be located in any suitable number of license plate detection zones 122. For example, multiple license plate detection zones 122 may be used to cover a desired area. A license plate detection zone 122 may refer to an area of coverage that is within the cameras' 120 field of view. The cameras 120 may be any suitable camera and/or video camera capable of capturing a set of images 123 that at least represent license plates of a vehicle 126 that enters the license plate detection zone 122. The set of images 123 may be transmitted by the camera 120 to the cloud-based computing system 116 and/or the computing device 102 via the network 112.[P-38]
With regards to the image capturing component 200, the component 200 may be configured to capture a set of images 123 within a license plate detection zone 122. At least some of the captured images 123 may represent license plates of a set of vehicles 126 appearing within the field of view of the cameras 120. The image capturing component 200 may configure one or more camera properties (e.g., zoom, focus, etc.) to obtain a clear image of the license plates. The image capturing component 200 may implement various techniques to extract the license plate ID from the images 123, or the image capturing component 200 may transmit the set of images 123, without analyzing the images 123, to the server 118 via the network 112.[P-48]
Therefore, it is obvious to one of ordinary sill in the art to combine Petrey’s teaching of a camera capable of detecting license plate information with Li modified’s teaching of a vehicle monitoring camera in order to improve by including a more effective camera to capture more driving scenarios adequately.
Allowable Subject Matter
Claims 10, 11, 14 and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 10 reads as “The method of claim 7 wherein the alert is transmitted, via wireless cellular network connection module of the vehicle traffic monitoring device or one or more other vehicle traffic monitoring devices within a selectable distance from the determined location of the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic, to respective mobile devices of the one or more drivers determined, based on the received location information, to be in a vehicle on the road and within a threshold proximity to the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic, wherein the threshold proximity is specific to each driver of the one or more drivers based on an estimated speed the driver is currently traveling.” During the time of the filing date of the said inventive limitations, there was no prior art that taught the scope of the invention in its entirety.
Claim 11, reads as, “The method of claim 7 wherein the alert is transmitted, via wireless cellular network connection module of the vehicle traffic monitoring device or one or more other vehicle traffic monitoring devices within a selectable distance from the determined location of the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic, to respective vehicles of the one or more drivers determined, based on the received location information, to be within a threshold proximity to the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic.” During the time of the filing date of the said inventive limitations, there was no prior art that taught the scope of the invention in its entirety.
Claim 14, reads as, “The method of claim 7 wherein the receiving location information includes: designating a cell in a cellular network to which the wireless cellular network connection module is connected as a potentially dangerous area based on the determined a location of the potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic being within a coverage area of the cell; and in response to the designating the cell as a potentially dangerous area, activating GMLC capability on mobile devices in the cellular network detected be within the coverage area of the designated cell in order to receive the data associated with respective vehicles or mobile devices of the plurality of drivers.” During the time of the filing date of the said inventive limitations, there was no prior art that taught the scope of the invention in its entirety.
Claim 17, reads as, “The method of claim 16 wherein the determining whether there exists a potential traffic hazard, traffic issue, safety issue or particular vehicle characteristic based on the detected high temperatures on one or more regions on the vehicle in traffic includes one or more of: detecting overheating of brakes of the vehicle in traffic based on the detected high temperatures on one or more regions on the vehicle in traffic; detecting overheating of an engine of the vehicle in traffic based on the detected high temperatures on one or more regions on the vehicle in traffic; and detecting overheating of one or more tires of the vehicle in traffic based on the detected high temperatures on one or more regions on the vehicle in traffic..” During the time of the filing date of the said inventive limitations, there was no prior art that taught the scope of the invention in its entirety.
Response to Arguments
The examiner acknowledges the applicant amendments, Though the amendments contact subject matter from previous dependent claim 15, claim 15, was previously dependent on claim 7. Thereby, integrating the elements of claim 15 into independent claim 1, without its correlation with claim 7, change the scope of the limitation and its interpretation. Therefore, due to this difference, the examiner has addressed the claimed limitations above under new grounds of rejection.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to 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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