DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-21 are rejected under 35 U.S.C. 102(a)1 and 102(a)2 as being anticipated by VENKATA`129, Pub. No.: US 20240182129 A1.
Regarding claim 1, VENKATA`129; discloses a method comprising:
storing road characteristic data, including a road characteristic at a point in a road ([0043] “in the sense the image capturing unit 214 starts recording as soon as the first end user opens the drive monitoring and analyzing module 114 before the live media is captured. ... the portion of data stored in the memory unit 218. The information stored in memory unit 218 may be preserved at least until an acknowledgment of receipt is received representing successful transmission through the communication link.” & [0069] “Secondary memory 830 may store the data software instructions” & [0070] Some or all of the data and instructions may be provided on removable storage unit 840” & [0071] “removable storage unit 840 includes a computer readable (storage) medium having stored therein computer software and/or data.” & [0072] ““computer program product” is used to generally refer to removable storage unit 840 or hard disk installed in hard drive 835.”);
receiving motorcycle vector data of a motorcycle traveling toward the point in the road ([0052] “the received information includes the gyroscope, speed, acceleration of the first end user. The first end user may be a cyclist or rider or driver of other two-wheeler or three vehicles. … The drive analyzing module 322 may receive information from the acceleration detection module, gyroscope detection module, image processing module and analyze received information.” & [0061] “set of sensors of the driving assistance device to detect the gyroscope, acceleration, speed, direction, and location of the first end user.” & [0063] “at step 704, collecting deceleration, gyroscope, yaw, pitch, and roll value … at step 706, collecting speed, geolocation coordinates and data from the accelerometer and gyroscope of the first computing device.”).
determining an anticipated motorcycle passenger position at the point in the road ([0049] “FIG. 3, …The drive monitoring and analyzing module 114 includes a bus 301, a drive monitoring module 302, an acceleration detection module 304, a gyroscope detection module 306, a position detection module 308, a movement tracking module 310, a location detection module 312, a navigation applications 314, an image capturing module 316, an image processing module 318, a turn predicting module 320, a drive analyzing module 322, an alert generating module 324.”); and
notifying a motorcycle passenger of the anticipated motorcycle passenger position before the motorcycle arrives at the point in the road ([0052] The turn predicting module 320 may be configured to predict the lean angle of the cyclist on the road based on the received information includes the gyroscope, speed, acceleration of the first end user. The first end user may be a cyclist or rider or driver of other two-wheeler or three vehicles. The turn predicting module 320 may configured to transmit predict the lean angle of the cyclist on the road. The drive analyzing module 322 may receive information from the acceleration detection module, gyroscope detection module, image processing module and analyze received information.”).
Regarding claim 2, VENKATA`129; discloses the method as in claim 1, wherein the anticipated motorcycle passenger position comprises a position selected from: small lean left, small lean right, small brace forward, small brace rearward, large lean left, large lean right, large brace forward, large brace rearward, and center position ([0052] The turn predicting module 320 may be configured to predict the lean angle of the cyclist on the road based on the received information includes the gyroscope, speed, acceleration of the first end user. The first end user may be a cyclist or rider or driver of other two-wheeler or three vehicles. The turn predicting module 320 may configured to transmit predict the lean angle of the cyclist on the road. … The drive analyzing module 322 may be configured to calculate the rate of change of deceleration(jerk), lean angle, and rate of change of angular displacement. … The drive analyzing module 322 may be configured to identify the difference between the predict lean angle of the cyclist on road and calculated lean angle of the cyclist on road.”).
Regarding claim 3, VENKATA`129; discloses the method as in claim 1, wherein the motorcycle vector data comprises speed, acceleration, or deceleration ([0041] The first set of sensors 204a, 204b, and 204c may be electrically coupled to the processing device 203 and is configured to measure the linear acceleration and angular acceleration of an object/subject at each point. … The speed sensor may be configured to detect the object's speed. The Object may include but not limited a car, bike, cycle. ... The speed sensor may be configured to measure the speed of the vehicle. The vehicle may include but not limited to, bike, car and so forth.).
Regarding claim 4, VENKATA`129; discloses the method as in claim 1, comprising notifying a motorcycle driver of the anticipated motorcycle passenger position before the motorcycle arrives at the point in the road ([0043] “The network module 216 may be configured to send the turn signals as notifications to the second end user. The notifications include audio and haptic prompts, including but not limited to, SMS, alerts, email, warnings, and so forth. The network module 216 may also be configured to send a geographical location as a communication link and the information identifying the location of the objects\subjects” & [0063] The method commences at step 702, detecting whether a first end user receives navigation guidance instruction from google application programming interface. ... Thereafter at step 710, analysing whether the calculated values fall in the range of the predicted values from the machine learning model. Thereafter at step 712, determine if banking angle of the rider going around the curve is in the range. If the answer at step 712 is Yes, the method continuous at step 714, analyzing the direction of turn with the calculated values and predicted values. Thereafter at step 716, notifying the users with the direction of turn through audio or haptic or visual signaling including LED lights indication, indication in mobile application etc. If the answer at step 712 is No, method goes to step 704.).
Regarding claim 5, VENKATA`129; discloses the method as in claim 1, comprising notifying the motorcycle passenger of a period of time until the motorcycle passenger is to assume the anticipated motorcycle passenger position ([0063] “detecting whether a first end user receives navigation guidance instruction from google application programming interface. … notifying the users with the direction of turn through audio or haptic or visual signaling including LED lights indication, indication in mobile application etc.”).
Regarding claim 6, VENKATA`129; discloses the method as in claim 1, wherein the notifying a motorcycle passenger of the anticipated motorcycle passenger position comprises sounding an audible message ([0063] “at step 716, notifying the users with the direction of turn through audio or haptic or visual signaling including LED lights indication, indication in mobile application etc.”).
Regarding claims 7 & 14, VENKATA`129 discloses
a controller & a guidance host (controller + circuits) comprising a processor and memory ([0038] The processing device 104 may include, but not limited to, a microcontroller (for example ARM 7 or ARM 11), a raspberry pi3 or a Pine 64 or any other 64 bit processor which can run Linux OS, a microprocessor, a digital signal processor, a microcomputer” & [0039] “The driving assistance device 102 includes the processing device 203, … a memory unit 218”);
a road characteristic circuit to transmit road characteristic data, including a road characteristic at a point in a road, to the controller ([0038] The processing device 104 may include,… a state machine or logic circuitry” & [0076] “hardware circuits, hardware chips, etc.” & [0051] “The driving monitoring and analyzing module 114 may be associated with the google maps API (application programming interface) 314 configured to provide navigation guidance instructions. The first end user may get turn-by-turn voice guided instructions on how to at arrive at a given destination through the google maps API (application programming interface) 314. …The image capturing module 316 may be configured to move the media files of the bending of cyclist/biker in a curved road with respect time to time.” & [0052] “The drive analyzing module 322 may receive information from the acceleration detection module, gyroscope detection module, image processing module and analyze received information. … to determine the banking angle and radius of curvature of the road or track. The drive analyzing module 322 may be configured to identify the difference between the predict lean angle of the cyclist on road and calculated lean angle of the cyclist on road.”).;
a motorcycle vector circuit to communicate motorcycle vector data, including the motorcycle's (real-time) vector relative to the point in the road, to the controller (([0030] “a system for driving monitoring and analyzing and generating alerts to users in real-time” & [0061] “establishing communication among the driving assistance device, the first computing device and the second computing device through the drive monitoring and analyzing module via the network. … calculating the lean angle of the object using gyroscope, acceleration, speed, and direction of the first end user by the driving assistance device. …transmitting calculated the lean angle of the object from the driving assistance device to the drive monitoring and analyzing module. … monitoring and analyzing module using gyroscope information, and acceleration information based on machine learning algorithm.”);
a motorcycle characteristic circuit to communicate a motorcycle characteristic, to the controller ([0032] “The driving assistance device 102 may be configured to activate the impact protocol (emergency protocol) to establish the communication with the first computing device 106 and the second computing device 108 through the drive monitoring and analyzing module 114 via the network 110.” & [0061] “establishing communication among the driving assistance device, the first computing device and the second computing device through the drive monitoring and analyzing module via the network.); and
a communication circuit to communicate a notification of an anticipated passenger position from the controller to a motorcycle passenger ([0063] “at step 716, notifying the users with the direction of turn through audio or haptic or visual signaling including LED lights indication, indication in mobile application etc.”),
wherein the memory comprises instructions which when executed by the processor are to determine an anticipated passenger position at the point in the road based on the road characteristic at the point in the road, the motorcycle's (real-time) vector relative to the point in the road, and the vehicle characteristic (([0030] “a system for driving monitoring and analyzing and generating alerts to users in real-time” & [0051] “The driving monitoring and analyzing module 114 may be associated with the google maps API (application programming interface) 314 configured to provide navigation guidance instructions.” & [0063] “detecting whether a first end user receives navigation guidance instruction from google application programming interface.” & [0064] “operative by execution of appropriate software instructions.” & [0065] “Digital processing system 800 may contain one or more processors such as a central processing unit (CPU) 810, random access memory (RAM) 820, secondary memory 830, graphics controller 860, display unit 870, network interface 880, and input interface 890.” & [0066] “CPU 810 may execute instructions stored in RAM 820 to provide several features of the present disclosure.” & [0067] “RAM 820 may receive instructions from secondary memory 830 using communication path 850. RAM 820 is shown currently containing software instructions” & [0061] “establishing communication”).
(claim 14) a guidance client (communication circuit+ communication interface), comprising: a communication circuit to receive the notification from the guidance host; and a communication interface to communicate the notification from the communication circuit to a motorcycle passenger ([0043] “The network module 216 may be configured to send the turn signals as notifications to the second end user. The notifications include audio and haptic prompts, including but not limited to, SMS, alerts, email, warnings, and so forth. The network module 216 may also be configured to send a geographical location as a communication link and the information identifying the location of the objects\subjects” & [0063] The method commences at step 702, detecting whether a first end user receives navigation guidance instruction from google application programming interface. ... Thereafter at step 710, analysing whether the calculated values fall in the range of the predicted values from the machine learning model. Thereafter at step 712, determine if banking angle of the rider going around the curve is in the range. If the answer at step 712 is Yes, the method continuous at step 714, analyzing the direction of turn with the calculated values and predicted values. Thereafter at step 716, notifying the users with the direction of turn through audio or haptic or visual signaling including LED lights indication, indication in mobile application etc. If the answer at step 712 is No, method goes to step 704.” & [0065] Digital processing system 800 may contain one or more processors such as a central processing unit (CPU) 810, random access memory (RAM) 820, secondary memory 830, graphics controller 860, display unit 870, network interface 880, and input interface 890.” & [0066] CPU 810 may execute instructions stored in RAM 820 to provide several features” & [0067] RAM 820 may receive instructions from secondary memory 830 using communication path 850. RAM 820 is shown currently containing software instructions”).
Regarding claims 8 & 15, VENKATA`129; discloses the device as in claim 7 & the system as in claim 14, wherein the road characteristic is selected from: road radius of curvature, road bank angle, road inclination, road declination, road hazard, road surface, road weather condition, and road traffic condition ([0052] “The drive analyzing module 322 may receive information from the acceleration detection module, gyroscope detection module, image processing module and analyze received information. The drive analyzing module 322 may be configured to calculate the rate of change of deceleration(jerk), lean angle, and rate of change of angular displacement. The drive analyzing module 322 may be configured to analyze the yaw, pitch and roll data with machine learning techniques and the deep learning techniques to determine the banking angle and radius of curvature of the road or track.” & [0053] “The first end user or rider may make to left or right turn, a lean angle θ is made by the rider with respect to the ground surface. The frictional force between the track's surface and the rider's tire of the cycle or bike” & [0059] The safe speed of the rider during the turn is determined by the banking angle and radius of curvature of the road or track. The cycle or the bike begins to slide outward if the speed is higher than this safe speed, but frictional force kicks in and adds more centripetal force to stop the outward skidding. Additionally, if the cycle or the bike is moving at a speed that is just a little bit too fast, it begins to skid inward.”).
Regarding claims 9 & 16, VENKATA`129; discloses the device as in claim 7 & the system as in claim 14, wherein the road characteristic circuit is to receive road data in real time from a sensor associated with the motorcycle or from a database comprising previously charted road data ([0030] “a system for driving monitoring and analyzing and generating alerts to users in real-time” & [0051] The position detection module 308 may be configured to fetch the object/subject positions (geo-location) “x” seconds before the object turns on the road and “x” seconds after the object turns on the road. … The location detection module 312 may be configured to provide accurate location of the turning of the road. The driving monitoring and analyzing module 114 may be associated with the google maps API (application programming interface) 314 configured to provide navigation guidance instructions. The first end user may get turn-by-turn voice guided instructions on how to at arrive at a given destination through the google maps API (application programming interface) 314. The first end user may include but not limited to the driver, rider, cyclist and so forth. The image capturing module 316 may be configured to capture the objects/subjects. The image capturing module 316 may be configured to move the media files of the bending of cyclist/biker in a curved road with respect time to time. The media files may include but not limited to, image, pictures, videos, GIF's and so forth.” & [0052] The turn predicting module 320 may be configured to predict the lean angle of the cyclist on the road based on the received information includes the gyroscope, speed, acceleration of the first end user. ... The drive analyzing module 322 may receive information from the acceleration detection module, gyroscope detection module, image processing module and analyze received information. … The drive analyzing module 322 may be configured to analyze the yaw, pitch and roll data with machine learning techniques and the deep learning techniques to determine the banking angle and radius of curvature of the road or track. The drive analyzing module 322 may be configured to identify the difference between the predict lean angle of the cyclist on road and calculated lean angle of the cyclist on road.”).
Regarding claims 10 & 17, VENKATA`129; discloses the device as in claim 7 & the system as in claim 14, wherein the motorcycle vector data comprises GPS data or data provided by a sensor associated with the motorcycle ([0039] “The driving assistance device 102 includes the processing device 203, a first set of sensors 204a, 204b, and 204c, a second set of sensors 206a, 206b and 206c, a third set of sensors 208a, 208b, and 208c, an motion detection unit 210, a GPS module 212, an image capturing unit 214, a network module 216, a memory unit 218, and a display unit 220, a microphone 222, two speakers 224, LED lights 226, vibration motors 228.).
Regarding claims 11 & 18, VENKATA`129; discloses the device as in claim 7 & the system as in claim 14, wherein the motorcycle characteristic is selected from motorcycle lean angle, motorcycle braking, and motorcycle accelerating ([0052] The turn predicting module 320 may be configured to predict the lean angle of the cyclist on the road based on the received information includes the gyroscope, speed, acceleration of the first end user. The first end user may be a cyclist or rider or driver of other two-wheeler or three vehicles. ... The drive analyzing module 322 may be configured to calculate the rate of change of deceleration(jerk), lean angle, and rate of change of angular displacement. The drive analyzing module 322 may be configured to analyze the yaw, pitch and roll data with machine learning techniques and the deep learning techniques to determine the banking angle and radius of curvature of the road or track.”). & [0052] “The turn predicting module 320 may be configured to predict the lean angle of the cyclist on the road based on the received information includes the gyroscope, speed, acceleration of the first end user. The first end user may be a cyclist or rider or driver of other two-wheeler or three vehicles. The turn predicting module 320 may configured to transmit predict the lean angle of the cyclist on the road. The drive analyzing module 322 may receive information from the acceleration detection module, gyroscope detection module, image processing module and analyze received information.”).
Regarding claims 12 & 19, VENKATA`129; discloses the device as in claim 7 & the system as in claim 14, wherein the notification comprises an audible message ([0063] “at step 716, notifying the users with the direction of turn through audio or haptic or visual signaling including LED lights indication, indication in mobile application etc.”).
Regarding claims 13 & 20, VENKATA`129; discloses the device as in claim 7 & the system as in claim 14, comprising a communication circuit to communicate a notification from the controller to a motorcycle driver ([0032] “The driving assistance device 102 may be configured to activate the impact protocol (emergency protocol) to establish the communication with the first computing device 106 and the second computing device 108 through the drive monitoring and analyzing module 114 via the network 110. … The drive monitoring and analyzing module 114 may be configured to establish the communication between the impact event monitoring device 102 and the first computing device 106 through the network 110” & [0038] The processing device 104 may include, but not limited to, a microcontroller … a programmable logic device, a state machine or logic circuitry” & [0076] “hardware circuits, hardware chips, etc.”).
Regarding claim 21, VENKATA`129; discloses the method as in claim 1, comprising providing a plurality of riding modes from which a riding mode may be selected (0032] “The driving assistance device 102 may be configured to detect and track an object's motion in three-dimensional space, … The driving assistance device 102 may be configured to detect/sense the impact events, emergency events, leaning & turning events, physical motion & movement interrupts, impacts or anomalies occur to the objects/subjects.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See Notice of References Cited.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jalal C CODUROGLU whose telephone number is (408)918-7527. The examiner can normally be reached Monday -Friday 8-6 PT.
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/Jalal C CODUROGLU/Examiner, Art Unit 3665
/DONALD J WALLACE/Primary Examiner, Art Unit 3665
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